This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 2 different clustering approaches and 4 clinical features across 17 patients, no significant finding detected with P value < 0.05.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Table 1. Get Full Table Overview of the association between subtypes identified by 2 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, no significant finding detected.
Clinical Features |
Statistical Tests |
MIRseq CNMF subtypes |
MIRseq cHierClus subtypes |
Time to Death | logrank test | 0.484 | 0.531 |
AGE | ANOVA | 0.277 | 0.831 |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1 |
NEOADJUVANT THERAPY | Fisher's exact test | 0.511 | 0.245 |
Table S1. Get Full Table Description of clustering approach #1: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
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Number of samples | 9 | 4 | 4 |
P value = 0.484 (logrank test)
Table S2. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
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ALL | 17 | 4 | 0.3 - 101.8 (28.9) |
subtype1 | 9 | 2 | 0.3 - 101.8 (28.9) |
subtype2 | 4 | 1 | 1.2 - 30.4 (20.3) |
subtype3 | 4 | 1 | 8.8 - 95.1 (70.4) |
Figure S1. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.277 (ANOVA)
Table S3. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 51.1 (13.3) |
subtype1 | 9 | 52.8 (14.2) |
subtype2 | 4 | 42.0 (9.4) |
subtype3 | 4 | 56.5 (12.6) |
Figure S2. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 1 (Fisher's exact test)
Table S4. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
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ALL | 11 | 6 |
subtype1 | 6 | 3 |
subtype2 | 2 | 2 |
subtype3 | 3 | 1 |
Figure S3. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V3.png)
P value = 0.511 (Fisher's exact test)
Table S5. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
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ALL | 11 | 6 |
subtype1 | 7 | 2 |
subtype2 | 2 | 2 |
subtype3 | 2 | 2 |
Figure S4. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
![](D1V4.png)
Table S6. Get Full Table Description of clustering approach #2: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 3 | 1 | 3 | 2 | 8 |
P value = 0.531 (logrank test)
Table S7. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
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ALL | 14 | 3 | 0.3 - 95.1 (29.7) |
subtype1 | 3 | 0 | 1.2 - 30.4 (12.4) |
subtype3 | 3 | 1 | 8.8 - 95.1 (69.9) |
subtype5 | 8 | 2 | 0.3 - 70.8 (32.8) |
Figure S5. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.831 (ANOVA)
Table S8. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
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ALL | 14 | 53.4 (13.0) |
subtype1 | 3 | 51.7 (7.2) |
subtype3 | 3 | 57.7 (15.1) |
subtype5 | 8 | 52.4 (14.9) |
Figure S6. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 1 (Fisher's exact test)
Table S9. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
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ALL | 10 | 4 |
subtype1 | 2 | 1 |
subtype3 | 2 | 1 |
subtype5 | 6 | 2 |
Figure S7. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V3.png)
P value = 0.245 (Fisher's exact test)
Table S10. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 10 | 4 |
subtype1 | 2 | 1 |
subtype3 | 1 | 2 |
subtype5 | 7 | 1 |
Figure S8. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
![](D2V4.png)
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Cluster data file = CESC.mergedcluster.txt
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Clinical data file = CESC.clin.merged.picked.txt
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Number of patients = 17
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Number of clustering approaches = 2
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Number of selected clinical features = 4
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R
For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.