This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 6 different clustering approaches and 4 clinical features across 127 patients, one significant finding detected with P value < 0.05.
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3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based miR expression data identified 4 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 6 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.
Clinical Features |
Time to Death |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
NEOADJUVANT THERAPY |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Fisher's exact test |
CN CNMF | 1 | 0.113 | 0.852 | 0.815 |
METHLYATION CNMF | 1 | 0.00809 | 1 | 0.385 |
RNAseq CNMF subtypes | 1 | 0.132 | 0.861 | 0.546 |
RNAseq cHierClus subtypes | 1 | 0.103 | 1 | 0.129 |
MIRseq CNMF subtypes | 1 | 0.195 | 0.758 | 0.161 |
MIRseq cHierClus subtypes | 1 | 0.877 | 0.306 | 0.579 |
Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 28 | 58 | 40 |
P value = 1 (logrank test)
Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 126 | 1 | 0.3 - 66.0 (18.4) |
subtype1 | 28 | 0 | 0.3 - 63.3 (13.8) |
subtype2 | 58 | 0 | 1.0 - 65.9 (20.7) |
subtype3 | 40 | 1 | 0.9 - 66.0 (22.9) |
Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.113 (ANOVA)
Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 126 | 60.9 (6.7) |
subtype1 | 28 | 62.3 (6.1) |
subtype2 | 58 | 59.6 (7.3) |
subtype3 | 40 | 62.0 (5.9) |
Figure S2. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.852 (Fisher's exact test)
Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 121 |
subtype1 | 1 | 27 |
subtype2 | 3 | 55 |
subtype3 | 1 | 39 |
Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.815 (Fisher's exact test)
Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 4 | 122 |
subtype1 | 0 | 28 |
subtype2 | 2 | 56 |
subtype3 | 2 | 38 |
Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Table S6. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 38 | 38 | 51 |
P value = 1 (logrank test)
Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 127 | 1 | 0.3 - 66.0 (18.2) |
subtype1 | 38 | 0 | 0.3 - 65.9 (23.8) |
subtype2 | 38 | 0 | 1.1 - 54.9 (15.6) |
subtype3 | 51 | 1 | 1.0 - 66.0 (19.5) |
Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.00809 (ANOVA)
Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 127 | 61.0 (6.7) |
subtype1 | 38 | 62.6 (5.8) |
subtype2 | 38 | 58.2 (7.3) |
subtype3 | 51 | 61.9 (6.3) |
Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

P value = 1 (Fisher's exact test)
Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 122 |
subtype1 | 1 | 37 |
subtype2 | 2 | 36 |
subtype3 | 2 | 49 |
Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.385 (Fisher's exact test)
Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 4 | 123 |
subtype1 | 1 | 37 |
subtype2 | 0 | 38 |
subtype3 | 3 | 48 |
Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Table S11. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 34 | 31 | 37 |
P value = 1 (logrank test)
Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 102 | 1 | 0.3 - 66.0 (16.8) |
subtype1 | 34 | 0 | 0.3 - 65.9 (19.6) |
subtype2 | 31 | 0 | 1.1 - 54.9 (13.0) |
subtype3 | 37 | 1 | 1.0 - 66.0 (17.1) |
Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.132 (ANOVA)
Table S13. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 102 | 61.3 (6.8) |
subtype1 | 34 | 62.4 (6.0) |
subtype2 | 31 | 59.3 (7.4) |
subtype3 | 37 | 62.0 (6.6) |
Figure S10. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.861 (Fisher's exact test)
Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 97 |
subtype1 | 1 | 33 |
subtype2 | 2 | 29 |
subtype3 | 2 | 35 |
Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.546 (Fisher's exact test)
Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 4 | 98 |
subtype1 | 2 | 32 |
subtype2 | 0 | 31 |
subtype3 | 2 | 35 |
Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Table S16. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 28 | 32 | 42 |
P value = 1 (logrank test)
Table S17. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 102 | 1 | 0.3 - 66.0 (16.8) |
subtype1 | 28 | 0 | 1.0 - 65.9 (19.6) |
subtype2 | 32 | 1 | 1.0 - 66.0 (14.8) |
subtype3 | 42 | 0 | 0.3 - 54.9 (15.1) |
Figure S13. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.103 (ANOVA)
Table S18. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 102 | 61.3 (6.8) |
subtype1 | 28 | 61.7 (6.2) |
subtype2 | 32 | 63.1 (6.8) |
subtype3 | 42 | 59.8 (6.9) |
Figure S14. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 1 (Fisher's exact test)
Table S19. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 97 |
subtype1 | 1 | 27 |
subtype2 | 2 | 30 |
subtype3 | 2 | 40 |
Figure S15. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.129 (Fisher's exact test)
Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 4 | 98 |
subtype1 | 1 | 27 |
subtype2 | 3 | 29 |
subtype3 | 0 | 42 |
Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Table S21. Get Full Table Description of clustering approach #5: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 39 | 37 | 21 | 28 |
P value = 1 (logrank test)
Table S22. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 125 | 1 | 0.3 - 66.0 (18.2) |
subtype1 | 39 | 0 | 0.9 - 65.9 (22.1) |
subtype2 | 37 | 0 | 3.0 - 54.9 (19.9) |
subtype3 | 21 | 0 | 0.3 - 64.1 (21.8) |
subtype4 | 28 | 1 | 1.1 - 66.0 (9.4) |
Figure S17. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.195 (ANOVA)
Table S23. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 61.0 (6.6) |
subtype1 | 39 | 62.6 (6.2) |
subtype2 | 37 | 59.3 (6.8) |
subtype3 | 21 | 61.0 (6.1) |
subtype4 | 28 | 61.2 (7.1) |
Figure S18. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.758 (Fisher's exact test)
Table S24. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 120 |
subtype1 | 2 | 37 |
subtype2 | 2 | 35 |
subtype3 | 1 | 20 |
subtype4 | 0 | 28 |
Figure S19. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.161 (Fisher's exact test)
Table S25. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 122 |
subtype1 | 3 | 36 |
subtype2 | 0 | 37 |
subtype3 | 0 | 21 |
subtype4 | 0 | 28 |
Figure S20. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Table S26. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 21 | 67 | 37 |
P value = 1 (logrank test)
Table S27. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 125 | 1 | 0.3 - 66.0 (18.2) |
subtype1 | 21 | 0 | 0.3 - 64.1 (23.8) |
subtype2 | 67 | 0 | 0.9 - 65.9 (23.0) |
subtype3 | 37 | 1 | 1.0 - 66.0 (8.5) |
Figure S21. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.877 (ANOVA)
Table S28. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 61.0 (6.6) |
subtype1 | 21 | 60.5 (5.8) |
subtype2 | 67 | 61.0 (6.9) |
subtype3 | 37 | 61.4 (6.7) |
Figure S22. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.306 (Fisher's exact test)
Table S29. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 5 | 120 |
subtype1 | 1 | 20 |
subtype2 | 4 | 63 |
subtype3 | 0 | 37 |
Figure S23. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.579 (Fisher's exact test)
Table S30. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 122 |
subtype1 | 0 | 21 |
subtype2 | 3 | 64 |
subtype3 | 0 | 37 |
Figure S24. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

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Cluster data file = PRAD.mergedcluster.txt
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Clinical data file = PRAD.clin.merged.picked.txt
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Number of patients = 127
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Number of clustering approaches = 6
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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.