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pmid:32324791

Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact.

Anna A Schönherz · Julie Støve Bødker · Alexander Schmitz · Rasmus Froberg Brøndum · Lasse Hjort Jakobsen · Anne Stidsholt Roug · Marianne T Severinsen · Tarec C El-Galaly · Paw Jensen · Hans Erik Johnsen · Martin Bøgsted · Karen Dybkær

PloS one · 2020 doi:10.1371/journal.pone.0229593

Paper

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title
Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact.
doi
10.1371/journal.pone.0229593
pmid
32324791
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Anna A Schönherz
[1]
Julie Støve Bødker
[2]
Alexander Schmitz
[3]
Rasmus Froberg Brøndum
[4]
Lasse Hjort Jakobsen
[5]
Anne Stidsholt Roug
[6]
Marianne T Severinsen
[7]
Tarec C El-Galaly
[8]
Paw Jensen
[9]
Hans Erik Johnsen
[10]
Martin Bøgsted
[11]
Karen Dybkær
journal
PloS one
year
2020
abstract
Acute myeloid leukaemia (AML) is characterised by phenotypic heterogeneity, which we hypothesise is a consequence of deregulated differentiation with transcriptional reminiscence of the normal compartment or cell-of-origin. Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34+/CD38- for haematopoietic stem cells (HSCs), CD34+/CD38+/CD45RA- for megakaryocyte-erythroid progenitors (MEPs), and CD34+/CD38+/CD45RA+ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. We present a differentiation-dependent classification system for AML subtypes with distinct pathogenetic and prognostic importance that can help identify candidates poorly responding to combination chemotherapy and potentially guide alternative treatments.
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The MAGS classifier for the HSC, MEP, and GMP subsets was obtained from regularised multinomial regression with the cell type as the discrete outcome and the median-centred GEP of the training cohort as the explanatory variable. The model was fitted with an elastic net penalty.
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The MAGS classifier for the HSC, MEP, and GMP subsets was obtained from regularised multinomial regression with the cell type as the discrete outcome and the median-centred GEP of the training cohort as the explanatory variable. The model was fitted with an elastic net penalty.
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17810
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Homo sapiens
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Expression profiles of normal hematopoietic stem and progenitor cells and acute myeloid leukemia sub-populations
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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All relevant data are within the manuscript and its Supporting Information files.
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GSM768754
sample_count
537
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Acute myeloid leukemia samples of samples =< 60yrs on HG-U133 plus 2
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[7] object · 16 fields
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asset:table:pmid:32324791:1
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section[10]/table-wrap[1]#pone.0229593.t001
version
null
note
MAGS prediction accuracy assigning 100% (A) or 85% (B) of the samples to the defined MAGS subtypes HSC, GMP, MEP, and an additional UC subtype.
structure object · 5 fields
pmcid
PMC7179860
section_index
10
section_title
Generation and validation of MAGS
row_count
10
column_count
6
table object · 8 fields
label
Table 1
caption
MAGS prediction accuracy assigning 100% (A) or 85% (B) of the samples to the defined MAGS subtypes HSC, GMP, MEP, and an additional UC subtype.
rows array · 10 items
[0] array · 4 items
[0]
A)
[1]
[2]
Predicted
[3]
Invalid Prediction (%)
[1] array · 6 items
[0]
Observed
[1]
[2]
HSC (%)
[3]
MEP (%)
[4]
GMP (%)
[5]
UC (%)
[2] array · 6 items
[0]
HSC (N = 26)
[1]
18 (69.2)
[2]
4 (15.4)
[3]
4 (15.4)
[4]
-
[5]
8 (30.8)
[3] array · 6 items
[0]
MEP (N = 5)
[1]
0 (0)
[2]
5 (100)
[3]
0 (0)
[4]
-
[5]
0 (0)
[4] array · 6 items
[0]
GMP (N = 7)
[1]
0 (0)
[2]
0 (0)
[3]
7 (100)
[4]
-
[5]
0 (0)
[5] array · 4 items
[0]
B)
[1]
[2]
Predicted
[3]
Invalid Prediction (%)
[6] array · 6 items
[0]
Observed
[1]
[2]
HSC (%)
[3]
MEP (%)
[4]
GMP (%)
[5]
UC (%)
[7] array · 6 items
[0]
HSC (N = 26)
[1]
17 (65.4)
[2]
3 (11.5)
[3]
0 (0)
[4]
6 (23.1)
[5]
9 (34.6)
[8] array · 6 items
[0]
MEP (N = 5)
[1]
0 (0)
[2]
5 (100)
[3]
0 (0)
[4]
0 (0)
[5]
0 (0)
[9] array · 6 items
[0]
GMP (N = 7)
[1]
0 (0)
[2]
0 (0)
[3]
7 (100)
[4]
0 (0)
[5]
0 (0)
locator
section[10]/table-wrap[1]#pone.0229593.t001
[8] object · 16 fields
id
asset:table:pmid:32324791:2
role
table
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jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:41ae1ff84260f8a631edb61f04f046e5df3119baf943f0230b416270d02f7d09
bytes
610
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[11]/table-wrap[1]#pone.0229593.t002
version
null
note
Distributions and frequencies of assigned MAGS subtypes across two clinical cohorts: TCGA (N = 182) and GSE6891 (N = 520).Two-sided Fishers exact tests were used to determine significantly different distributions across data sets (p = 0.99).
structure object · 5 fields
pmcid
PMC7179860
section_index
11
section_title
MAGS assignment of clinical samples and prognostic impact
row_count
3
column_count
6
table object · 8 fields
label
Table 2
caption
Distributions and frequencies of assigned MAGS subtypes across two clinical cohorts: TCGA (N = 182) and GSE6891 (N = 520).Two-sided Fishers exact tests were used to determine significantly different distributions across data sets (p = 0.99).
rows array · 3 items
[0] array · 6 items
[0]
Cohort
[1]
HSC (%)
[2]
MEP (%)
[3]
GMP (%)
[4]
UC (%)
[5]
Total (%)
[1] array · 6 items
[0]
TCGA
[1]
50 (27.5)
[2]
48 (26.4)
[3]
56 (30.8)
[4]
28 (15.4)
[5]
182 (100)
[2] array · 6 items
[0]
GSE6891
[1]
146 (28.1)
[2]
134 (25.8)
[3]
162 (31.2)
[4]
78 (15.0)
[5]
520 (100)
locator
section[11]/table-wrap[1]#pone.0229593.t002
[9] object · 16 fields
id
asset:table:pmid:32324791:3
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:e1435be04c29712a8ccb4c73782a04ff5a4a1c9775aa00f2df517802c4133e73
bytes
1403
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[11]/table-wrap[2]#pone.0229593.t003
version
null
note
Univariate Cox regression analysis for MAGS-assigned clinical cohorts (GSE6891 cohort: N = 520; TCGA-cohort: N = 171) and the corresponding AML meta-cohort (N = 691) that included both clinical cohorts.Analyses were performed for overall survival. Samples with missing survival information (TCGA cohort: N = 11) were excluded. Table columns indicate the total sample size (N), the number of patients that died (Events) per MAGS subtype, the associated hazards ration (HR), the 95% lower and upper confidence intervals (CI), and the estimated p-value (P).
structure object · 5 fields
pmcid
PMC7179860
section_index
11
section_title
MAGS assignment of clinical samples and prognostic impact
row_count
16
column_count
6
table object · 8 fields
label
Table 3
caption
Univariate Cox regression analysis for MAGS-assigned clinical cohorts (GSE6891 cohort: N = 520; TCGA-cohort: N = 171) and the corresponding AML meta-cohort (N = 691) that included both clinical cohorts.Analyses were performed for overall survival. Samples with missing survival information (TCGA cohort: N = 11) were excluded. Table columns indicate the total sample size (N), the number of patients that died (Events) per MAGS subtype, the associated hazards ration (HR), the 95% lower and upper confidence intervals (CI), and the estimated p-value (P).
rows array · 16 items
[0] array · 6 items
[0]
[1]
N
[2]
Events
[3]
HR
[4]
95% CI
[5]
P
[1] array · 6 items
[0]
TCGA cohort
[1]
[2]
[3]
[4]
[5]
[2] array · 6 items
[0]
HSC
[1]
48
[2]
40
[3]
1
[4]
[5]
[3] array · 6 items
[0]
MEP
[1]
44
[2]
22
[3]
0.44
[4]
0.26–0.74
[5]
1.90e-03**
[4] array · 6 items
[0]
GMP
[1]
52
[2]
34
[3]
0.68
[4]
0.43–1.08
[5]
0.1
[5] array · 6 items
[0]
UC
[1]
27
[2]
14
[3]
0.37
[4]
0.20–0.69
[5]
1.63e-03**
[6] array · 6 items
[0]
GSE6891 cohort
[1]
[2]
[3]
[4]
[5]
[7] array · 6 items
[0]
HSC
[1]
146
[2]
115
[3]
1
[4]
[5]
[8] array · 6 items
[0]
MEP
[1]
134
[2]
77
[3]
0.53
[4]
0.40–0.71
[5]
2.13e-05***
[9] array · 6 items
[0]
GMP
[1]
162
[2]
101
[3]
0.63
[4]
0.48–0.83
[5]
7.86e-04***
[10] array · 6 items
[0]
UC
[1]
78
[2]
52
[3]
0.70
[4]
0.51–0.98
[5]
0.03
[11] array · 6 items
[0]
Meta-cohorta
[1]
[2]
[3]
[4]
[5]
[12] array · 6 items
[0]
HSC
[1]
194
[2]
155
[3]
1
[4]
[5]
[13] array · 6 items
[0]
MEP
[1]
178
[2]
99
[3]
0.51
[4]
0.40–0.65
[5]
1.46e-07***
[14] array · 6 items
[0]
GMP
[1]
214
[2]
135
[3]
0.64
[4]
0.51–0.81
[5]
1.64e-04***
[15] array · 6 items
[0]
UC
[1]
105
[2]
66
[3]
0.60
[4]
0.45–0.80
[5]
5.32e-04***
locator
section[11]/table-wrap[2]#pone.0229593.t003
[10] object · 16 fields
id
asset:table:pmid:32324791:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:d616118d0c6f505e19ab2e800a7ed0eff0b4edc69f33f7b696a02a58395ded1c
bytes
8121
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[11]/table-wrap[3]#pone.0229593.t004
version
null
note
Cox regression analysis of potential confounding variables conducted for the TCGA (N = 122), GSE6891 (N = 439), and meta-cohort (N = 561).Cohorts were limited to samples with complete records for all explanatory variables investigated. Results are shown for the (A) univariate Cox regression analysis per explanatory variable and (B) associated multivariate Cox regression analyses limited to confounding variables tested significant in univariate Cox regression analyses. Table columns are as described in Table 3. Analyses were performed for overall survival.
structure object · 5 fields
pmcid
PMC7179860
section_index
11
section_title
MAGS assignment of clinical samples and prognostic impact
row_count
69
column_count
17
table object · 8 fields
label
Table 4
caption
Cox regression analysis of potential confounding variables conducted for the TCGA (N = 122), GSE6891 (N = 439), and meta-cohort (N = 561).Cohorts were limited to samples with complete records for all explanatory variables investigated. Results are shown for the (A) univariate Cox regression analysis per explanatory variable and (B) associated multivariate Cox regression analyses limited to confounding variables tested significant in univariate Cox regression analyses. Table columns are as described in Table 3. Analyses were performed for overall survival.
rows array · 69 items
[0] array · 3 items
[0]
A) Simple Cox Regression
[1]
[2]
[1] array · 4 items
[0]
[1]
TCGA cohort (N = 122)
[2]
GSE6891 cohort (N = 439)
[3]
AML meta-cohort (N = 561)
[2] array · 17 items
[0]
[1]
[2]
N
[3]
Events
[4]
HR
[5]
95% CI
[6]
P
[7]
N
[8]
Events
[9]
HR
[10]
95% CI
[11]
P
[12]
N
[13]
Events
[14]
HR
[15]
95% CI
[16]
P
[3] array · 17 items
[0]
MAGS
[1]
HSC
[2]
37
[3]
32
[4]
1
[5]
[6]
[7]
120
[8]
93
[9]
1
[10]
[11]
[12]
157
[13]
125
[14]
1
[15]
[16]
[4] array · 16 items
[0]
MEP
[1]
26
[2]
15
[3]
0.523
[4]
0.282–0.971
[5]
0.040*
[6]
108
[7]
56
[8]
0.491
[9]
0.352–0.685
[10]
2.75E-05***
[11]
134
[12]
71
[13]
0.486
[14]
0.363–0.650
[15]
1.26E-06***
[5] array · 16 items
[0]
GMP
[1]
40
[2]
26
[3]
0.595
[4]
0.353–1.005
[5]
0.052
[6]
147
[7]
89
[8]
0.632
[9]
0.472–0.846
[10]
0.002**
[11]
187
[12]
115
[13]
0.622
[14]
0.483–0.802
[15]
2.50E-04***
[6] array · 16 items
[0]
UC
[1]
19
[2]
9
[3]
0.309
[4]
0.146–0.652
[5]
0.002**
[6]
64
[7]
40
[8]
0.666
[9]
0.460–0.965
[10]
0.032*
[11]
83
[12]
49
[13]
0.580
[14]
0.417–0.807
[15]
0.001**
[7] array · 17 items
[0]
Cytogenetic riska
[1]
GOOD
[2]
24
[3]
10
[4]
1
[5]
[6]
[7]
97
[8]
40
[9]
1
[10]
[11]
[12]
NA
[13]
NA
[14]
NA
[15]
NA
[16]
NA
[8] array · 16 items
[0]
INTER
[1]
60
[2]
42
[3]
2.636
[4]
1.319–5.268
[5]
0.006**
[6]
247
[7]
160
[8]
1.900
[9]
1.341–2.690
[10]
3.02E-04***
[11]
NA
[12]
NA
[13]
NA
[14]
NA
[15]
NA
[9] array · 16 items
[0]
POOR
[1]
35
[2]
27
[3]
2.828
[4]
1.362–5.872
[5]
0.005**
[6]
85
[7]
69
[8]
3.222
[9]
2.173–4.778
[10]
5.84E-09***
[11]
NA
[12]
NA
[13]
NA
[14]
NA
[15]
NA
[10] array · 16 items
[0]
UC
[1]
3
[2]
3
[3]
8.159
[4]
2.188–30.431
[5]
0.002**
[6]
10
[7]
9
[8]
3.801
[9]
1.839–7.857
[10]
3.14E-04***
[11]
NA
[12]
NA
[13]
NA
[14]
NA
[15]
NA
[11] array · 17 items
[0]
FABb
[1]
M0
[2]
13
[3]
9
[4]
1
[5]
[6]
[7]
16
[8]
13
[9]
1
[10]
[11]
[12]
29
[13]
22
[14]
1
[15]
[16]
[12] array · 16 items
[0]
M1
[1]
28
[2]
21
[3]
1.773
[4]
0.805–3.904
[5]
0.155
[6]
94
[7]
60
[8]
0.646
[9]
0.355–1.177
[10]
0.153
[11]
122
[12]
81
[13]
0.772
[14]
0.481–1.236
[15]
0.281
[13] array · 16 items
[0]
M2
[1]
27
[2]
17
[3]
1.156
[4]
0.520–2.659
[5]
0.697
[6]
104
[7]
64
[8]
0.629
[9]
0.346–1.142
[10]
0.128
[11]
131
[12]
81
[13]
0.698
[14]
0.435–1.119
[15]
0.135
[14] array · 16 items
[0]
M3
[1]
6
[2]
3
[3]
0.542
[4]
0.146–2.013
[5]
0.360
[6]
24
[7]
11
[8]
0.469
[9]
0.210–1.048
[10]
0.065
[11]
30
[12]
14
[13]
0.492
[14]
0.251–0.962
[15]
0.038*
[15] array · 16 items
[0]
M4
[1]
27
[2]
16
[3]
0.861
[4]
0.379–1.955
[5]
0.720
[6]
79
[7]
48
[8]
0.635
[9]
0.344–1.173
[10]
0.147
[11]
106
[12]
64
[13]
0.683
[14]
0.420–1.108
[15]
0.123
[16] array · 16 items
[0]
M4E
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[5]
NA
[6]
5
[7]
2
[8]
0.325
[9]
0.073–1.444
[10]
0.140
[11]
5
[12]
2
[13]
0.330
[14]
0.077–1.403
[15]
0.133
[17] array · 16 items
[0]
M5
[1]
14
[2]
10
[3]
1.957
[4]
0.785–4.879
[5]
0.150
[6]
103
[7]
72
[8]
0.780
[9]
0.432–1.407
[10]
0.408
[11]
117
[12]
82
[13]
0.846
[14]
0.528–1.356
[15]
0.488
[18] array · 16 items
[0]
M6
[1]
3
[2]
3
[3]
2.452
[4]
0.649–9.272
[5]
0.186
[6]
6
[7]
3
[8]
0.415
[9]
0.118–1.456
[10]
0.170
[11]
9
[12]
6
[13]
0.702
[14]
0.284–1.731
[15]
0.442
[19] array · 16 items
[0]
M7
[1]
3
[2]
3
[3]
2.311
[4]
0.615–8.679
[5]
0.215
[6]
0
[7]
0
[8]
NA
[9]
NA
[10]
NA
[11]
3
[12]
3
[13]
1.920
[14]
0.573–6.429
[15]
0.290
[20] array · 16 items
[0]
UC
[1]
1
[2]
0
[3]
2.751
[4]
NA
[5]
0.996
[6]
8
[7]
5
[8]
0.869
[9]
0.310–2.438
[10]
0.790
[11]
9
[12]
5
[13]
0.865
[14]
0.328–2.286
[15]
0.771
[21] array · 17 items
[0]
CEBPA
[1]
NEG
[2]
119
[3]
80
[4]
1
[5]
[6]
[7]
406
[8]
262
[9]
1
[10]
[11]
[12]
525
[13]
342
[14]
1
[15]
[16]
[22] array · 16 items
[0]
POS
[1]
3
[2]
2
[3]
1.184
[4]
0.289–4.851
[5]
0.815
[6]
29
[7]
13
[8]
0.555
[9]
0.318–0.968
[10]
0.038*
[11]
32
[12]
15
[13]
0.556
[14]
0.332–0.933
[15]
0.026*
[23] array · 17 items
[0]
FLT3-ITD
[1]
NEG
[2]
121
[3]
81
[4]
1
[5]
[6]
[7]
315
[8]
187
[9]
1
[10]
[11]
[12]
436
[13]
268
[14]
1
[15]
[16]
[24] array · 16 items
[0]
POS
[1]
1
[2]
1
[3]
5.724
[4]
0.766–42.766
[5]
0.089
[6]
124
[7]
91
[8]
1.665
[9]
1.294–2.141
[10]
7.18E-05***
[11]
125
[12]
92
[13]
1.428
[14]
1.127–1.811
[15]
0.003**
[25] array · 17 items
[0]
FLT3-TKD
[1]
NEG
[2]
107
[3]
71
[4]
1
[5]
[6]
[7]
390
[8]
254
[9]
1
[10]
[11]
[12]
497
[13]
325
[14]
1
[15]
[16]
[26] array · 16 items
[0]
POS
[1]
15
[2]
11
[3]
1.478
[4]
0.778–2.810
[5]
0.233
[6]
48
[7]
24
[8]
0.699
[9]
0.460–1.062
[10]
0.093
[11]
63
[12]
35
[13]
0.820
[14]
0.579–1.163
[15]
0.265
[27] array · 17 items
[0]
IDH1
[1]
NEG
[2]
116
[3]
78
[4]
1
[5]
[6]
[7]
405
[8]
256
[9]
1
[10]
[11]
[12]
521
[13]
334
[14]
1
[15]
[16]
[28] array · 16 items
[0]
POS
[1]
6
[2]
4
[3]
0.772
[4]
0.282–2.114
[5]
0.614
[6]
31
[7]
19
[8]
0.901
[9]
0.565–1.436
[10]
0.661
[11]
37
[12]
23
[13]
0.863
[14]
0.565–1.316
[15]
0.493
[29] array · 17 items
[0]
IDH2
[1]
NEG
[2]
113
[3]
78
[4]
1
[5]
[6]
[7]
400
[8]
254
[9]
1
[10]
[11]
[12]
513
[13]
332
[14]
1
[15]
[16]
[30] array · 16 items
[0]
POS
[1]
9
[2]
4
[3]
0.509
[4]
0.186–1.394
[5]
0.189
[6]
36
[7]
21
[8]
0.849
[9]
0.544–1.324
[10]
0.470
[11]
45
[12]
25
[13]
0.765
[14]
0.509–1.148
[15]
0.196
[31] array · 17 items
[0]
KRAS
[1]
NEG
[2]
116
[3]
78
[4]
1
[5]
[6]
[7]
436
[8]
276
[9]
1
[10]
[11]
[12]
552
[13]
354
[14]
1
[15]
[16]
[32] array · 16 items
[0]
POS
[1]
6
[2]
4
[3]
0.669
[4]
0.244–1.836
[5]
0.435
[6]
3
[7]
2
[8]
1.028
[9]
0.256–4.132
[10]
0.970
[11]
9
[12]
6
[13]
1.031
[14]
0.460–2.311
[15]
0.941
[33] array · 17 items
[0]
NMP1
[1]
NEG
[2]
109
[3]
73
[4]
1
[5]
[6]
[7]
303
[8]
198
[9]
1
[10]
[11]
[12]
412
[13]
271
[14]
1
[15]
[16]
[34] array · 16 items
[0]
POS
[1]
13
[2]
9
[3]
1.739
[4]
0.864–3.501
[5]
0.121
[6]
136
[7]
80
[8]
0.874
[9]
0.674–1.133
[10]
0.310
[11]
149
[12]
89
[13]
0.845
[14]
0.665–1.074
[15]
0.169
[35] array · 17 items
[0]
NRAS
[1]
NEG
[2]
115
[3]
77
[4]
1
[5]
[6]
[7]
396
[8]
253
[9]
1
[10]
[11]
[12]
511
[13]
330
[14]
1
[15]
[16]
[36] array · 16 items
[0]
POS
[1]
7
[2]
5
[3]
1.435
[4]
0.578–3.563
[5]
0.436
[6]
42
[7]
25
[8]
0.826
[9]
0.548–1.246
[10]
0.362
[11]
49
[12]
30
[13]
0.843
[14]
0.580–1.225
[15]
0.370
[37] array · 17 items
[0]
WBC
[1]
WBC
[2]
122
[3]
82
[4]
1.002
[5]
0.998–1.006
[6]
0.402
[7]
NA
[8]
NA
[9]
NA
[10]
NA
[11]
NA
[12]
NA
[13]
NA
[14]
NA
[15]
NA
[16]
NA
[38] array · 17 items
[0]
Age
[1]
Age
[2]
122
[3]
82
[4]
1.041
[5]
1.022–1.060
[6]
1.44E-05***
[7]
439
[8]
278
[9]
1.015
[10]
1.005–1.026
[11]
0.003**
[12]
561
[13]
360
[14]
1.025
[15]
1.017–1.033
[16]
2.64E-09***
[39] array · 17 items
[0]
Cohort
[1]
TCGA
[2]
NA
[3]
NA
[4]
NA
[5]
NA
[6]
NA
[7]
NA
[8]
NA
[9]
NA
[10]
NA
[11]
NA
[12]
122
[13]
82
[14]
1
[15]
[16]
[40] array · 16 items
[0]
GSE6891
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[5]
NA
[6]
NA
[7]
NA
[8]
NA
[9]
NA
[10]
NA
[11]
439
[12]
278
[13]
0.579
[14]
0.451–0.744
[15]
1.99E-05***
[41] array · 3 items
[0]
B) Multivariate Cox Regression
[1]
[2]
[42] array · 5 items
[0]
[1]
[2]
TCGA cohort (N = 122)
[3]
GSE6891 cohort (N = 439)
[4]
AML meta-cohort (N = 561)
[43] array · 17 items
[0]
[1]
[2]
N
[3]
Events
[4]
HR
[5]
95% CI
[6]
P
[7]
N
[8]
Events
[9]
HR
[10]
95% CI
[11]
P
[12]
N
[13]
Events
[14]
HR
[15]
95% CI
[16]
P
[44] array · 17 items
[0]
MAGS
[1]
HSC
[2]
37
[3]
32
[4]
1
[5]
[6]
[7]
120
[8]
93
[9]
1
[10]
[11]
[12]
157
[13]
125
[14]
1
[15]
[16]
[45] array · 16 items
[0]
MEP
[1]
26
[2]
15
[3]
0.781
[4]
0.404–1.511
[5]
0.464
[6]
108
[7]
56
[8]
0.600
[9]
0.419–0.862
[10]
0.006**
[11]
134
[12]
71
[13]
0.524
[14]
0.374–0.734
[15]
1.69E-04***
[46] array · 16 items
[0]
GMP
[1]
40
[2]
26
[3]
0.780
[4]
0.445–1.365
[5]
0.383
[6]
147
[7]
89
[8]
0.723
[9]
0.537–0.973
[10]
0.032*
[11]
187
[12]
115
[13]
0.593
[14]
0.443–0.793
[15]
4.32E-04***
[47] array · 16 items
[0]
UC
[1]
19
[2]
9
[3]
0.467
[4]
0.213–1.025
[5]
0.058*
[6]
64
[7]
40
[8]
0.742
[9]
0.508–1.084
[10]
0.123
[11]
83
[12]
49
[13]
0.561
[14]
0.398–0.791
[15]
9.55E-04***
[48] array · 17 items
[0]
Cytogenetic riska
[1]
GOOD
[2]
24
[3]
10
[4]
1
[5]
[6]
[7]
97
[8]
40
[9]
1
[10]
[11]
[12]
-
[13]
-
[14]
-
[15]
-
[16]
-
[49] array · 16 items
[0]
INTER
[1]
60
[2]
42
[3]
1.996
[4]
0.964–4.129
[5]
0.063
[6]
247
[7]
160
[8]
1.529
[9]
1.045–2.239
[10]
0.029
[11]
-
[12]
-
[13]
-
[14]
-
[15]
-
[50] array · 16 items
[0]
POOR
[1]
35
[2]
27
[3]
1.911
[4]
0.870–4.195
[5]
0.107
[6]
85
[7]
69
[8]
2.751
[9]
1.825–4.147
[10]
1.36E-06***
[11]
-
[12]
-
[13]
-
[14]
-
[15]
-
[51] array · 16 items
[0]
UC
[1]
3
[2]
3
[3]
7.440
[4]
1.928–28,712
[5]
0.004**
[6]
10
[7]
9
[8]
3.036
[9]
1.445–6.379
[10]
0.003**
[11]
-
[12]
-
[13]
-
[14]
-
[15]
-
[52] array · 17 items
[0]
FABb
[1]
M0
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
-
[12]
29
[13]
22
[14]
1
[15]
[16]
[53] array · 16 items
[0]
M1
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
122
[12]
81
[13]
1.267
[14]
0.769–2.087
[15]
0.353
[54] array · 16 items
[0]
M2
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
131
[12]
81
[13]
1.266
[14]
0.766–2.094
[15]
0.358
[55] array · 16 items
[0]
M3
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
30
[12]
14
[13]
1.089
[14]
0.531–2.233
[15]
0.816
[56] array · 16 items
[0]
M4
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
106
[12]
64
[13]
1.143
[14]
0.684–1.911
[15]
0.610
[57] array · 16 items
[0]
M4E
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
5
[12]
2
[13]
0.861
[14]
0.196–3.790
[15]
0.844
[58] array · 16 items
[0]
M5
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
117
[12]
82
[13]
1.595
[14]
0.934–2.723
[15]
0.087
[59] array · 16 items
[0]
M6
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
9
[12]
6
[13]
1.140
[14]
0.418–3.112
[15]
0.798
[60] array · 16 items
[0]
M7
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
3
[12]
3
[13]
2.006
[14]
0.573–7.021
[15]
0.276
[61] array · 16 items
[0]
UC
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
-
[8]
-
[9]
-
[10]
-
[11]
9
[12]
5
[13]
1.186
[14]
0.442–3.181
[15]
0.735
[62] array · 17 items
[0]
CEBPA
[1]
NEG
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
406
[8]
262
[9]
1
[10]
[11]
[12]
525
[13]
342
[14]
1
[15]
[16]
[63] array · 16 items
[0]
POS
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
29
[7]
13
[8]
0.703
[9]
0.386–1.283
[10]
0.251
[11]
32
[12]
15
[13]
0.759
[14]
0.433–1.331
[15]
0.336
[64] array · 17 items
[0]
FLT3-ITD
[1]
NEG
[2]
-
[3]
-
[4]
-
[5]
-
[6]
-
[7]
315
[8]
187
[9]
1
[10]
[11]
[12]
436
[13]
268
[14]
1
[15]
[16]
[65] array · 16 items
[0]
POS
[1]
-
[2]
-
[3]
-
[4]
-
[5]
-
[6]
124
[7]
91
[8]
1.621
[9]
1.230–2.135
[10]
6.04E-04***
[11]
125
[12]
92
[13]
1.542
[14]
1.193–1.994
[15]
9.52E-04***
[66] array · 17 items
[0]
Age
[1]
Age
[2]
122
[3]
82
[4]
1.035
[5]
1.017–1.054
[6]
1.71E-04***
[7]
435
[8]
275
[9]
1.011
[10]
1.001–1.022
[11]
0.036*
[12]
557
[13]
357
[14]
1.021
[15]
1.012–1.030
[16]
4.56E-06***
[67] array · 17 items
[0]
Cohort
[1]
TCGA
[2]
NA
[3]
NA
[4]
NA
[5]
NA
[6]
NA
[7]
NA
[8]
NA
[9]
NA
[10]
NA
[11]
NA
[12]
122
[13]
82
[14]
1
[15]
[16]
[68] array · 16 items
[0]
GSE6891
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[5]
NA
[6]
NA
[7]
NA
[8]
NA
[9]
NA
[10]
NA
[11]
439
[12]
278
[13]
0.640
[14]
0.472–0.869
[15]
0.005**
locator
section[11]/table-wrap[3]#pone.0229593.t004
[11] object · 16 fields
id
asset:table:pmid:32324791:5
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:d728a681f3957405306352a241379871e30665639209dff220b9db24770e2d5d
bytes
1382
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[12]/table-wrap[1]#pone.0229593.t005
version
null
note
Overview of enrichment patterns detected using gene set enrichment analysis (GSEA) in the reduced assignment probability meta-cohort (N = 573: NGSE6891 = 423, NTCGA = 150), investigating the Hallmark, the C2-CP, and the C3-TFT gene set collections retrieved from the MSigDB.GSEA analysis was conducted for three comparisons: (A) HSC vs. Rest, (B) MEP vs. Rest, and (C) GMP vs. Rest. The total number of enriched gene sets (Total) with p-value ≤ 0.01 and FDR ≤ 0.25, and the numbers of enriched gene sets detected per group within each comparison are presented.
structure object · 5 fields
pmcid
PMC7179860
section_index
12
section_title
DGE and functional annotation of enriched gene sets
row_count
15
column_count
4
table object · 8 fields
label
Table 5
caption
Overview of enrichment patterns detected using gene set enrichment analysis (GSEA) in the reduced assignment probability meta-cohort (N = 573: NGSE6891 = 423, NTCGA = 150), investigating the Hallmark, the C2-CP, and the C3-TFT gene set collections retrieved from the MSigDB.GSEA analysis was conducted for three comparisons: (A) HSC vs. Rest, (B) MEP vs. Rest, and (C) GMP vs. Rest. The total number of enriched gene sets (Total) with p-value ≤ 0.01 and FDR ≤ 0.25, and the numbers of enriched gene sets detected per group within each comparison are presented.
rows array · 15 items
[0] array · 1 item
[0]
A) HSC vs. Rest
[1] array · 4 items
[0]
MSigDB collection
[1]
Total
[2]
Enriched in HSC (%)
[3]
Enriched in Rest (%)
[2] array · 4 items
[0]
Hallmark
[1]
31
[2]
10 (32.3)
[3]
21 (67.7)
[3] array · 4 items
[0]
C2-CP
[1]
256
[2]
30 (11.7)
[3]
226 (88.3)
[4] array · 4 items
[0]
C3-TFT
[1]
222
[2]
173 (77.9)
[3]
49 (22.1)
[5] array · 1 item
[0]
B) MEP vs. Rest
[6] array · 4 items
[0]
MSigDB collection
[1]
Total
[2]
Enriched in GMP (%)
[3]
Enriched in Rest (%)
[7] array · 4 items
[0]
Hallmark
[1]
25
[2]
11 (44.0)
[3]
14 (56.0)
[8] array · 4 items
[0]
C2-CP
[1]
360
[2]
59 (16.4)
[3]
301 (83.6)
[9] array · 4 items
[0]
C3-TFT
[1]
101
[2]
32 (31.7)
[3]
69 (68.3)
[10] array · 1 item
[0]
C) GMP vs. Rest
[11] array · 4 items
[0]
MsigDB collection
[1]
Total
[2]
Enriched in MEP (%)
[3]
Enriched in Rest (%)
[12] array · 4 items
[0]
Hallmark
[1]
21
[2]
20 (95.2)
[3]
1 (4.8)
[13] array · 4 items
[0]
C2-CP
[1]
254
[2]
252 (99.2)
[3]
301 (0.8)
[14] array · 4 items
[0]
C3-TFT
[1]
22
[2]
9 (40.9)
[3]
69 (59.1)
locator
section[12]/table-wrap[1]#pone.0229593.t005
gaps array · 12 items
[0]
asset:data:geo:GSE17054 not downloaded
[1]
asset:data:geo:GSE17054 licence not read
[2]
asset:data:geo:GSE19429 not downloaded
[3]
asset:data:geo:GSE19429 licence not read
[4]
asset:data:geo:GSE19599 not downloaded
[5]
asset:data:geo:GSE19599 licence not read
[6]
asset:data:geo:GSE42519 not downloaded
[7]
asset:data:geo:GSE42519 licence not read
[8]
asset:data:geo:GSE63270 not downloaded
[9]
asset:data:geo:GSE63270 licence not read
[10]
asset:data:geo:GSE6891 not downloaded
[11]
asset:data:geo:GSE6891 licence not read
analysis object · 4 fields
state
partial
methods array · 5 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
kaplan_meier
label
Survival analysis
dsl_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
band
medium
quote
Cox regression
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log-rank
[3] object · 10 fields
id
method-4
family
contingency_analysis
specific
fisher_exact
label
Categorical (contingency table) analysis
dsl_operations array · 2 items
[0]
contingency_table
[1]
chi_square_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
categorical_cross_tabulation
evidence object · 3 fields
score
0.8
band
high
quote
Fishers exact
[4] object · 10 fields
id
method-5
family
null
specific
null
label
Regularised multinomial regression with an elastic net penalty
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
The MAGS classifier for the HSC, MEP, and GMP subsets was obtained from regularised multinomial regression with the cell type as the discrete outcome and the median-centred GEP of the training cohort as the explanatory variable. The model was fitted with an elastic net penalty.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 6 items
[0]
GSE63270
[1]
GSE42519
[2]
GSE19599
[3]
GSE17054
[4]
GSE19429
[5]
GSE6891
resolved_accessions array · 6 items
[0]
GSE63270
[1]
GSE42519
[2]
GSE19599
[3]
GSE17054
[4]
GSE19429
[5]
GSE6891
statements array · 1 item
[0]
All relevant data are within the manuscript and its Supporting Information files.
basis
The paper names GSE63270, GSE42519, GSE19599, GSE17054, GSE19429, GSE6891 in Gene Expression Omnibus Database (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
survival_km
assets array · 7 items
[0]
source:pmid:32324791
[1]
data:geo:GSE17054
[2]
asset:table:pmid:32324791:1
[3]
asset:table:pmid:32324791:2
[4]
asset:table:pmid:32324791:3
[5]
asset:table:pmid:32324791:4
[6]
asset:table:pmid:32324791:5
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE17054
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:19:28Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
44779
completion_tokens
2029
cached_tokens
784
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:32324791
type
paper
label
pmid:32324791
[1] object · 3 fields
id
source:pmid:32324791
type
manuscript
label
PMID 32324791 full text
[2] object · 3 fields
id
data:geo:GSE17054
type
dataset
label
GEO GSE17054
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:32324791
type
paper
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Complete file
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Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34+/CD38- for haematopoietic stem cells (HSCs), CD34+/CD38+/CD45RA- for megakaryocyte-erythroid progenitors (MEPs), and CD34+/CD38+/CD45RA+ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. 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"asset:table:pmid:32324791:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e9d46dab0d6bbf697c2de554520b4f2f9c8abfb03a57c9e8fa7dec7736a588de", "bytes": 871, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#pone.0229593.t001", "version": null, "note": "MAGS prediction accuracy assigning 100% (A) or 85% (B) of the samples to the defined MAGS subtypes HSC, GMP, MEP, and an additional UC subtype.", "structure": {"pmcid": "PMC7179860", "section_index": 10, "section_title": "Generation and validation of MAGS", "row_count": 10, "column_count": 6}, "table": {"label": "Table 1", "caption": "MAGS prediction accuracy assigning 100% (A) or 85% (B) of the samples to the defined MAGS subtypes HSC, GMP, MEP, and an additional UC subtype.", "rows": [["A)", "", "Predicted", "Invalid Prediction (%)"], ["Observed", "", "HSC (%)", "MEP (%)", "GMP (%)", "UC (%)"], ["HSC (N = 26)", "18 (69.2)", "4 (15.4)", "4 (15.4)", "-", "8 (30.8)"], ["MEP (N = 5)", "0 (0)", "5 (100)", "0 (0)", "-", "0 (0)"], ["GMP (N = 7)", "0 (0)", "0 (0)", "7 (100)", "-", "0 (0)"], ["B)", "", "Predicted", "Invalid Prediction (%)"], ["Observed", "", "HSC (%)", "MEP (%)", "GMP (%)", "UC (%)"], ["HSC (N = 26)", "17 (65.4)", "3 (11.5)", "0 (0)", "6 (23.1)", "9 (34.6)"], ["MEP (N = 5)", "0 (0)", "5 (100)", "0 (0)", "0 (0)", "0 (0)"], ["GMP (N = 7)", "0 (0)", "0 (0)", "7 (100)", "0 (0)", "0 (0)"]], "row_count": 10, "column_count": 6, "numeric_cells": 33, "header_rows": 0, "locator": "section[10]/table-wrap[1]#pone.0229593.t001"}}, {"id": "asset:table:pmid:32324791:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:41ae1ff84260f8a631edb61f04f046e5df3119baf943f0230b416270d02f7d09", "bytes": 610, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#pone.0229593.t002", "version": null, "note": "Distributions and frequencies of assigned MAGS subtypes across two clinical cohorts: TCGA (N = 182) and GSE6891 (N = 520).Two-sided Fishers exact tests were used to determine significantly different distributions across data sets (p = 0.99).", "structure": {"pmcid": "PMC7179860", "section_index": 11, "section_title": "MAGS assignment of clinical samples and prognostic impact", "row_count": 3, "column_count": 6}, "table": {"label": "Table 2", "caption": "Distributions and frequencies of assigned MAGS subtypes across two clinical cohorts: TCGA (N = 182) and GSE6891 (N = 520).Two-sided Fishers exact tests were used to determine significantly different distributions across data sets (p = 0.99).", "rows": [["Cohort", "HSC (%)", "MEP (%)", "GMP (%)", "UC (%)", "Total (%)"], ["TCGA", "50 (27.5)", "48 (26.4)", "56 (30.8)", "28 (15.4)", "182 (100)"], ["GSE6891", "146 (28.1)", "134 (25.8)", "162 (31.2)", "78 (15.0)", "520 (100)"]], "row_count": 3, "column_count": 6, "numeric_cells": 11, "header_rows": 1, "locator": "section[11]/table-wrap[1]#pone.0229593.t002"}}, {"id": "asset:table:pmid:32324791:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e1435be04c29712a8ccb4c73782a04ff5a4a1c9775aa00f2df517802c4133e73", "bytes": 1403, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[2]#pone.0229593.t003", "version": null, "note": "Univariate Cox regression analysis for MAGS-assigned clinical cohorts (GSE6891 cohort: N = 520; TCGA-cohort: N = 171) and the corresponding AML meta-cohort (N = 691) that included both clinical cohorts.Analyses were performed for overall survival. Samples with missing survival information (TCGA cohort: N = 11) were excluded. Table columns indicate the total sample size (N), the number of patients that died (Events) per MAGS subtype, the associated hazards ration (HR), the 95% lower and upper confidence intervals (CI), and the estimated p-value (P).", "structure": {"pmcid": "PMC7179860", "section_index": 11, "section_title": "MAGS assignment of clinical samples and prognostic impact", "row_count": 16, "column_count": 6}, "table": {"label": "Table 3", "caption": "Univariate Cox regression analysis for MAGS-assigned clinical cohorts (GSE6891 cohort: N = 520; TCGA-cohort: N = 171) and the corresponding AML meta-cohort (N = 691) that included both clinical cohorts.Analyses were performed for overall survival. Samples with missing survival information (TCGA cohort: N = 11) were excluded. Table columns indicate the total sample size (N), the number of patients that died (Events) per MAGS subtype, the associated hazards ration (HR), the 95% lower and upper confidence intervals (CI), and the estimated p-value (P).", "rows": [["", "N", "Events", "HR", "95% CI", "P"], ["TCGA cohort", "", "", "", "", ""], ["HSC", "48", "40", "1", "", ""], ["MEP", "44", "22", "0.44", "0.26–0.74", "1.90e-03**"], ["GMP", "52", "34", "0.68", "0.43–1.08", "0.1"], ["UC", "27", "14", "0.37", "0.20–0.69", "1.63e-03**"], ["GSE6891 cohort", "", "", "", "", ""], ["HSC", "146", "115", "1", "", ""], ["MEP", "134", "77", "0.53", "0.40–0.71", "2.13e-05***"], ["GMP", "162", "101", "0.63", "0.48–0.83", "7.86e-04***"], ["UC", "78", "52", "0.70", "0.51–0.98", "0.03"], ["Meta-cohorta", "", "", "", "", ""], ["HSC", "194", "155", "1", "", ""], ["MEP", "178", "99", "0.51", "0.40–0.65", "1.46e-07***"], ["GMP", "214", "135", "0.64", "0.51–0.81", "1.64e-04***"], ["UC", "105", "66", "0.60", "0.45–0.80", "5.32e-04***"]], "row_count": 16, "column_count": 6, "numeric_cells": 56, "header_rows": 1, "locator": "section[11]/table-wrap[2]#pone.0229593.t003"}}, {"id": "asset:table:pmid:32324791:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:d616118d0c6f505e19ab2e800a7ed0eff0b4edc69f33f7b696a02a58395ded1c", "bytes": 8121, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[3]#pone.0229593.t004", "version": null, "note": "Cox regression analysis of potential confounding variables conducted for the TCGA (N = 122), GSE6891 (N = 439), and meta-cohort (N = 561).Cohorts were limited to samples with complete records for all explanatory variables investigated. Results are shown for the (A) univariate Cox regression analysis per explanatory variable and (B) associated multivariate Cox regression analyses limited to confounding variables tested significant in univariate Cox regression analyses. Table columns are as described in Table 3. Analyses were performed for overall survival.", "structure": {"pmcid": "PMC7179860", "section_index": 11, "section_title": "MAGS assignment of clinical samples and prognostic impact", "row_count": 69, "column_count": 17}, "table": {"label": "Table 4", "caption": "Cox regression analysis of potential confounding variables conducted for the TCGA (N = 122), GSE6891 (N = 439), and meta-cohort (N = 561).Cohorts were limited to samples with complete records for all explanatory variables investigated. Results are shown for the (A) univariate Cox regression analysis per explanatory variable and (B) associated multivariate Cox regression analyses limited to confounding variables tested significant in univariate Cox regression analyses. Table columns are as described in Table 3. Analyses were performed for overall survival.", "rows": [["A) Simple Cox Regression", "", ""], ["", "TCGA cohort (N = 122)", "GSE6891 cohort (N = 439)", "AML meta-cohort (N = 561)"], ["", "", "N", "Events", "HR", "95% CI", "P", "N", "Events", "HR", "95% CI", "P", "N", "Events", "HR", "95% CI", "P"], ["MAGS", "HSC", "37", "32", "1", "", "", "120", "93", "1", "", "", "157", "125", "1", "", ""], ["MEP", "26", "15", "0.523", "0.282–0.971", "0.040*", "108", "56", "0.491", "0.352–0.685", "2.75E-05***", "134", "71", "0.486", "0.363–0.650", "1.26E-06***"], ["GMP", "40", "26", "0.595", "0.353–1.005", "0.052", "147", "89", "0.632", "0.472–0.846", "0.002**", "187", "115", "0.622", "0.483–0.802", "2.50E-04***"], ["UC", "19", "9", "0.309", "0.146–0.652", "0.002**", "64", "40", "0.666", "0.460–0.965", "0.032*", "83", "49", "0.580", "0.417–0.807", "0.001**"], ["Cytogenetic riska", "GOOD", "24", "10", "1", "", "", "97", "40", "1", "", "", "NA", "NA", "NA", "NA", "NA"], ["INTER", "60", "42", "2.636", 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"NA", "NA", "122", "82", "1", "", ""], ["GSE6891", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "439", "278", "0.579", "0.451–0.744", "1.99E-05***"], ["B) Multivariate Cox Regression", "", ""], ["", "", "TCGA cohort (N = 122)", "GSE6891 cohort (N = 439)", "AML meta-cohort (N = 561)"], ["", "", "N", "Events", "HR", "95% CI", "P", "N", "Events", "HR", "95% CI", "P", "N", "Events", "HR", "95% CI", "P"], ["MAGS", "HSC", "37", "32", "1", "", "", "120", "93", "1", "", "", "157", "125", "1", "", ""], ["MEP", "26", "15", "0.781", "0.404–1.511", "0.464", "108", "56", "0.600", "0.419–0.862", "0.006**", "134", "71", "0.524", "0.374–0.734", "1.69E-04***"], ["GMP", "40", "26", "0.780", "0.445–1.365", "0.383", "147", "89", "0.723", "0.537–0.973", "0.032*", "187", "115", "0.593", "0.443–0.793", "4.32E-04***"], ["UC", "19", "9", "0.467", "0.213–1.025", "0.058*", "64", "40", "0.742", "0.508–1.084", "0.123", "83", "49", "0.561", "0.398–0.791", "9.55E-04***"], ["Cytogenetic riska", "GOOD", 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"1.71E-04***", "435", "275", "1.011", "1.001–1.022", "0.036*", "557", "357", "1.021", "1.012–1.030", "4.56E-06***"], ["Cohort", "TCGA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "122", "82", "1", "", ""], ["GSE6891", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "439", "278", "0.640", "0.472–0.869", "0.005**"]], "row_count": 69, "column_count": 17, "numeric_cells": 652, "header_rows": 0, "locator": "section[11]/table-wrap[3]#pone.0229593.t004"}}, {"id": "asset:table:pmid:32324791:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:d728a681f3957405306352a241379871e30665639209dff220b9db24770e2d5d", "bytes": 1382, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#pone.0229593.t005", "version": null, "note": "Overview of enrichment patterns detected using gene set enrichment analysis (GSEA) in the reduced assignment probability meta-cohort (N = 573: NGSE6891 = 423, NTCGA = 150), investigating the Hallmark, the C2-CP, and the C3-TFT gene set collections retrieved from the MSigDB.GSEA analysis was conducted for three comparisons: (A) HSC vs. Rest, (B) MEP vs. Rest, and (C) GMP vs. Rest. The total number of enriched gene sets (Total) with p-value ≤ 0.01 and FDR ≤ 0.25, and the numbers of enriched gene sets detected per group within each comparison are presented.", "structure": {"pmcid": "PMC7179860", "section_index": 12, "section_title": "DGE and functional annotation of enriched gene sets", "row_count": 15, "column_count": 4}, "table": {"label": "Table 5", "caption": "Overview of enrichment patterns detected using gene set enrichment analysis (GSEA) in the reduced assignment probability meta-cohort (N = 573: NGSE6891 = 423, NTCGA = 150), investigating the Hallmark, the C2-CP, and the C3-TFT gene set collections retrieved from the MSigDB.GSEA analysis was conducted for three comparisons: (A) HSC vs. Rest, (B) MEP vs. Rest, and (C) GMP vs. Rest. The total number of enriched gene sets (Total) with p-value ≤ 0.01 and FDR ≤ 0.25, and the numbers of enriched gene sets detected per group within each comparison are presented.", "rows": [["A) HSC vs. Rest"], ["MSigDB collection", "Total", "Enriched in HSC (%)", "Enriched in Rest (%)"], ["Hallmark", "31", "10 (32.3)", "21 (67.7)"], ["C2-CP", "256", "30 (11.7)", "226 (88.3)"], ["C3-TFT", "222", "173 (77.9)", "49 (22.1)"], ["B) MEP vs. Rest"], ["MSigDB collection", "Total", "Enriched in GMP (%)", "Enriched in Rest (%)"], ["Hallmark", "25", "11 (44.0)", "14 (56.0)"], ["C2-CP", "360", "59 (16.4)", "301 (83.6)"], ["C3-TFT", "101", "32 (31.7)", "69 (68.3)"], ["C) GMP vs. Rest"], ["MsigDB collection", "Total", "Enriched in MEP (%)", "Enriched in Rest (%)"], ["Hallmark", "21", "20 (95.2)", "1 (4.8)"], ["C2-CP", "254", "252 (99.2)", "301 (0.8)"], ["C3-TFT", "22", "9 (40.9)", "69 (59.1)"]], "row_count": 15, "column_count": 4, "numeric_cells": 27, "header_rows": 0, "locator": "section[12]/table-wrap[1]#pone.0229593.t005"}}], "gaps": ["asset:data:geo:GSE17054 not downloaded", "asset:data:geo:GSE17054 licence not read", "asset:data:geo:GSE19429 not downloaded", "asset:data:geo:GSE19429 licence not read", "asset:data:geo:GSE19599 not downloaded", "asset:data:geo:GSE19599 licence not read", "asset:data:geo:GSE42519 not downloaded", "asset:data:geo:GSE42519 licence not read", "asset:data:geo:GSE63270 not downloaded", "asset:data:geo:GSE63270 licence not read", "asset:data:geo:GSE6891 not downloaded", "asset:data:geo:GSE6891 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-2", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Cox regression"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "contingency_analysis", "specific": "fisher_exact", "label": "Categorical (contingency table) analysis", "dsl_operations": ["contingency_table", "chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "Fishers exact"}}, {"id": "method-5", "family": null, "specific": null, "label": "Regularised multinomial regression with an elastic net penalty", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The MAGS classifier for the HSC, MEP, and GMP subsets was obtained from regularised multinomial regression with the cell type as the discrete outcome and the median-centred GEP of the training cohort as the explanatory variable. The model was fitted with an elastic net penalty."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE63270", "GSE42519", "GSE19599", "GSE17054", "GSE19429", "GSE6891"], "resolved_accessions": ["GSE63270", "GSE42519", "GSE19599", "GSE17054", "GSE19429", "GSE6891"], "statements": ["All relevant data are within the manuscript and its Supporting Information files."], "basis": "The paper names GSE63270, GSE42519, GSE19599, GSE17054, GSE19429, GSE6891 in Gene Expression Omnibus Database (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:32324791", "data:geo:GSE17054", "asset:table:pmid:32324791:1", "asset:table:pmid:32324791:2", "asset:table:pmid:32324791:3", "asset:table:pmid:32324791:4", "asset:table:pmid:32324791:5"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE17054"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:19:28Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 44779, "completion_tokens": 2029, "cached_tokens": 784}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:32324791", "type": "paper", "label": "pmid:32324791"}, {"id": "source:pmid:32324791", "type": "manuscript", "label": "PMID 32324791 full text"}, {"id": "data:geo:GSE17054", "type": "dataset", "label": "GEO GSE17054"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:32324791", "type": "paper", "label": "pmid:32324791"}, {"id": "source:pmid:32324791", "type": "manuscript", "label": "PMID 32324791 full text"}, {"id": "data:geo:GSE17054", "type": "dataset", "label": "GEO GSE17054"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-32324791", "type": "extraction", "used": ["source:pmid:32324791"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE17054", "type": "accession_resolution", "used": ["data:geo:GSE17054"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:32324791", "parent_hash": "sha256:78a9748bbab7fa64b43ac994dc8d67bb5a6f0dee308ecdf1c93a299f90cb699a", "fork_of": null, "generation": 6}, "gaps": ["asset:data:geo:GSE17054 licence not read", "asset:data:geo:GSE17054 not downloaded", "asset:data:geo:GSE19429 licence not read", "asset:data:geo:GSE19429 not downloaded", "asset:data:geo:GSE19599 licence not read", "asset:data:geo:GSE19599 not downloaded", "asset:data:geo:GSE42519 licence not read", "asset:data:geo:GSE42519 not downloaded", "asset:data:geo:GSE63270 licence not read", "asset:data:geo:GSE63270 not downloaded", "asset:data:geo:GSE6891 licence not read", "asset:data:geo:GSE6891 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["level using a Brainarray custom CDF for the Affymetrix Human Genome U133 Plus 2.0 GeneChip (version"], "order_basis": "paper_text_order:2561", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Kaplan-Meier"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}