Download Clustering analysis of the learning set

Clustering analysis of tumor samples (IBC 20, nIBC 20) normals excluded
Hierarchical clustering analysis was performed with 1000 most informative
probes.
A
B
• Two clusters were formed.
cluster A -- many ER + cases (83%, 15 out of
18).
cluster B -- many ER - cases (59%, 13 out of
22).
• No cluster enriched in IBC was formed.
cluster A -- IBC is 61% (11 out of 18).
cluster B -- IBC is 41% (9 out of 22).
A cluster rich in IBC was not
formed.
sample type
ER
HER2
Figure. Results of hierarchical clustering analysis.
1000 probes having the greatest SD were chosen for unsupervised
hierarchical clustering analysis. High relative expression is green, low
relative expression is red. Sample type (IBC or nIBC) and ER/HER2
status (based on IHC) of the samples are color-coded as shown.
sample type (■: nIBC, ■: IBC)
HER2 (□: 0-1, ■: 2 (FISH-), ■: 2(FISH+)-3)
5)
ER (□: 0, ■:1, ■:3-
1
(1) ASSESSMENT OF IBC BY mRNA EXPRESSION
Selection of the gene set for assessment of IBC
24 samples (IBC 12, non-IBC 12) were used as a learning set and the remaining 16
samples were used as validation set
<gene selection criteria>
1) differentially expressed genes
Filtered by the following criteria:
Gene A
Gene B
IBC nIBC
IBC nIBC
・ fold change ≧ 2
・ t-test (non-paired)p-value ＜ 0.05
6500
⇒ 779 genes were selected.
6050
4700
2) lower false-negative/false-positive rates
・Determination of the positive threshold in each
gene The value that gives the minimum of the sum of falsepositives and false-negatives. Gene A → 4700
2900
2750
Gene B → false negative 1, false positive 1 → ○
exclude
132 genes were selected as the gene set for assessment of IBC
2
• 100 genes were expressed at higher levels in IBC.
• 32 genes were expressed at lower levels in IBC.
1000
1200
Gene B → 2900
・False-positive/negative are detected in two or less
Gene A → false negative 5, false positive 0 → ×
samples
3800
select
(1) ASSESSMENT OF IBC BY mRNA EXPRESSION
Clustering analysis of the learning set (IBC 12, nIBC 12)
Hierarchical clustering analysis of the learning set was performed with 132 genes.
A
B
• Two clusters were formed.
IBC>nIBC genes
-- ER+ enriched cluster was not formed
cluster A -- ER+ ; 75% (9 out of 12)
cluster B -- ER+ ; 75% (9 out of 12)
• A cluster enriched for IBC was formed.
nIBC>IBC
genes
cluster A -- nIBC cluster (nIBC ; 100%, 12 out of
12)
cluster B -- IBC cluster (IBC ; 100%, 12 out of
12)
sample type
ER
HER2
Figure. Results of hierarchical clustering analysis.
Unsupervised hierarchical clustering was performed using 132 genes.
High relative expression is green, low relative expression is red.
Sample type (IBC or nIBC) and ER/HER2 status (based on IHC) of the
samples are color-coded as shown.
sample type (■: nIBC, ■: IBC)
HER2
3 (□: 0-1, ■: 2 (FISH-), ■: 2 (FISH+)-3)
ER (□: 0, ■:1, ■:3-5)
The learning set clustered according to
sample type (IBC / nIBC).
The selected 132 genes could discriminate
IBC from non-IBC.
(1) ASSESSMENT OF IBC BY mRNA EXPRESSION
Evaluation of the IBC assessment with the learning set
IBC-likeness was computed in each sample, and the IBC assessment was evaluated.
IBC score： Index of IBC-likeness of a sample
IBC-like
= The rate of the positive genes in each sample
1) Positive/Negative was judged for every gene
・ IBC＞nIBC genes
→ positive when higher than threshold
・ nIBC＞IBC genes
→ positive when lower than threshold
2) The IBC score for each sample was determined
by calculating the percentage of positive genes.
positive genes / 132 (all genes) x 100
IBC score
※ we defined ‘IBC score’ as an index of IBC-likeness
IBC diagnostic criteria
43.0
nIBC-like
nIBC
IBC
Figure. Distribution of the IBC score.
scores of the IBC group were higher and the two groups
were completely separated.
The learning set could be divided into IBC and nIBC by the IBC score.
4
• IBC diagnostic criteria was set to 43.0
The