(metastatic tumor cohort)
This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 3 clinical features across 144 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.
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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 do not correlate to any clinical features.
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CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death'.
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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 8 different clustering approaches and 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.
Clinical Features |
Time to Death |
AGE | GENDER |
Statistical Tests | logrank test | ANOVA | Fisher's exact test |
CN CNMF |
0.343 (1.00) |
0.304 (1.00) |
0.975 (1.00) |
METHLYATION CNMF |
0.0535 (1.00) |
0.249 (1.00) |
0.172 (1.00) |
RPPA CNMF subtypes |
0.0142 (0.327) |
0.0499 (1.00) |
0.704 (1.00) |
RPPA cHierClus subtypes |
0.0686 (1.00) |
0.899 (1.00) |
0.526 (1.00) |
RNAseq CNMF subtypes |
0.000585 (0.014) |
0.0536 (1.00) |
0.408 (1.00) |
RNAseq cHierClus subtypes |
0.0189 (0.416) |
0.0729 (1.00) |
0.467 (1.00) |
MIRseq CNMF subtypes |
0.135 (1.00) |
0.37 (1.00) |
0.609 (1.00) |
MIRseq cHierClus subtypes |
0.197 (1.00) |
0.398 (1.00) |
0.861 (1.00) |
Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 56 | 47 | 41 |
P value = 0.343 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 141 | 68 | 0.2 - 346.0 (47.5) |
subtype1 | 55 | 29 | 0.2 - 248.6 (41.6) |
subtype2 | 47 | 19 | 4.2 - 314.5 (46.8) |
subtype3 | 39 | 20 | 6.4 - 346.0 (58.8) |
Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.304 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 142 | 56.0 (16.2) |
subtype1 | 55 | 57.5 (18.0) |
subtype2 | 47 | 57.1 (13.7) |
subtype3 | 40 | 52.6 (16.3) |
Figure S2. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.975 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 51 | 93 |
subtype1 | 20 | 36 |
subtype2 | 16 | 31 |
subtype3 | 15 | 26 |
Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
Table S5. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 38 | 53 | 48 |
P value = 0.0535 (logrank test), Q value = 1
Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 136 | 67 | 0.2 - 346.0 (47.5) |
subtype1 | 38 | 25 | 0.2 - 204.6 (39.9) |
subtype2 | 51 | 22 | 2.6 - 248.6 (53.9) |
subtype3 | 47 | 20 | 2.7 - 346.0 (47.3) |
Figure S4. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.249 (ANOVA), Q value = 1
Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 137 | 56.5 (16.2) |
subtype1 | 38 | 55.3 (17.1) |
subtype2 | 51 | 59.4 (16.6) |
subtype3 | 48 | 54.2 (14.7) |
Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.172 (Fisher's exact test), Q value = 1
Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 51 | 88 |
subtype1 | 14 | 24 |
subtype2 | 24 | 29 |
subtype3 | 13 | 35 |
Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
Table S9. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 17 | 17 | 17 | 27 | 19 |
P value = 0.0142 (logrank test), Q value = 0.33
Table S10. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 94 | 45 | 2.6 - 346.0 (47.3) |
subtype1 | 17 | 8 | 10.1 - 248.6 (55.9) |
subtype2 | 15 | 12 | 13.9 - 117.2 (39.6) |
subtype3 | 17 | 5 | 4.2 - 346.0 (54.7) |
subtype4 | 26 | 14 | 2.6 - 167.9 (43.4) |
subtype5 | 19 | 6 | 2.7 - 182.0 (41.6) |
Figure S7. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.0499 (ANOVA), Q value = 1
Table S11. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 95 | 56.7 (16.8) |
subtype1 | 17 | 46.4 (17.4) |
subtype2 | 16 | 61.1 (17.7) |
subtype3 | 17 | 60.2 (14.2) |
subtype4 | 26 | 59.7 (15.0) |
subtype5 | 19 | 54.8 (17.6) |
Figure S8. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.704 (Chi-square test), Q value = 1
Table S12. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 39 | 58 |
subtype1 | 6 | 11 |
subtype2 | 5 | 12 |
subtype3 | 9 | 8 |
subtype4 | 11 | 16 |
subtype5 | 8 | 11 |
Figure S9. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
Table S13. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 23 | 19 | 37 | 18 |
P value = 0.0686 (logrank test), Q value = 1
Table S14. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 94 | 45 | 2.6 - 346.0 (47.3) |
subtype1 | 23 | 6 | 4.2 - 346.0 (48.9) |
subtype2 | 18 | 11 | 6.4 - 204.6 (44.6) |
subtype3 | 36 | 18 | 2.6 - 216.9 (48.6) |
subtype4 | 17 | 10 | 13.9 - 117.2 (39.6) |
Figure S10. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.899 (ANOVA), Q value = 1
Table S15. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 95 | 56.7 (16.8) |
subtype1 | 23 | 58.3 (14.8) |
subtype2 | 18 | 54.3 (15.8) |
subtype3 | 36 | 56.9 (17.9) |
subtype4 | 18 | 56.4 (19.0) |
Figure S11. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.526 (Fisher's exact test), Q value = 1
Table S16. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 39 | 58 |
subtype1 | 12 | 11 |
subtype2 | 6 | 13 |
subtype3 | 15 | 22 |
subtype4 | 6 | 12 |
Figure S12. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
Table S17. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 51 | 40 | 50 |
P value = 0.000585 (logrank test), Q value = 0.014
Table S18. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 138 | 68 | 0.2 - 346.0 (47.5) |
subtype1 | 50 | 27 | 6.4 - 346.0 (61.3) |
subtype2 | 39 | 10 | 4.2 - 203.0 (53.3) |
subtype3 | 49 | 31 | 0.2 - 228.6 (35.9) |
Figure S13. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.0536 (ANOVA), Q value = 1
Table S19. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 139 | 56.1 (16.3) |
subtype1 | 50 | 51.7 (16.7) |
subtype2 | 40 | 57.8 (15.8) |
subtype3 | 49 | 59.1 (15.7) |
Figure S14. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.408 (Fisher's exact test), Q value = 1
Table S20. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 91 |
subtype1 | 21 | 30 |
subtype2 | 11 | 29 |
subtype3 | 18 | 32 |
Figure S15. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
Table S21. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 36 | 69 | 36 |
P value = 0.0189 (logrank test), Q value = 0.42
Table S22. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 138 | 68 | 0.2 - 346.0 (47.5) |
subtype1 | 35 | 21 | 7.8 - 346.0 (61.2) |
subtype2 | 68 | 25 | 2.7 - 248.6 (49.2) |
subtype3 | 35 | 22 | 0.2 - 228.6 (33.2) |
Figure S16. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.0729 (ANOVA), Q value = 1
Table S23. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 139 | 56.1 (16.3) |
subtype1 | 35 | 51.5 (15.5) |
subtype2 | 69 | 56.1 (16.3) |
subtype3 | 35 | 60.5 (16.5) |
Figure S17. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.467 (Fisher's exact test), Q value = 1
Table S24. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 91 |
subtype1 | 15 | 21 |
subtype2 | 21 | 48 |
subtype3 | 14 | 22 |
Figure S18. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
Table S25. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 31 | 50 | 31 | 22 | 2 |
P value = 0.135 (logrank test), Q value = 1
Table S26. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 132 | 65 | 0.2 - 346.0 (47.5) |
subtype1 | 31 | 14 | 17.0 - 346.0 (44.0) |
subtype2 | 49 | 28 | 2.6 - 216.9 (43.2) |
subtype3 | 31 | 17 | 7.8 - 314.5 (55.9) |
subtype4 | 21 | 6 | 0.2 - 248.6 (46.8) |
Figure S19. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.37 (ANOVA), Q value = 1
Table S27. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 132 | 56.2 (16.2) |
subtype1 | 31 | 54.5 (13.7) |
subtype2 | 49 | 59.0 (16.6) |
subtype3 | 31 | 52.8 (17.3) |
subtype4 | 21 | 56.8 (17.0) |
Figure S20. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.609 (Fisher's exact test), Q value = 1
Table S28. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 47 | 87 |
subtype1 | 13 | 18 |
subtype2 | 18 | 32 |
subtype3 | 8 | 23 |
subtype4 | 8 | 14 |
Figure S21. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
Table S29. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 16 | 89 | 31 |
P value = 0.197 (logrank test), Q value = 1
Table S30. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 134 | 66 | 0.2 - 346.0 (47.7) |
subtype1 | 15 | 3 | 0.2 - 203.0 (28.8) |
subtype2 | 88 | 49 | 2.6 - 314.5 (47.3) |
subtype3 | 31 | 14 | 10.1 - 346.0 (61.2) |
Figure S22. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.398 (ANOVA), Q value = 1
Table S31. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 134 | 56.1 (16.1) |
subtype1 | 15 | 58.5 (15.8) |
subtype2 | 88 | 56.9 (16.9) |
subtype3 | 31 | 52.8 (13.7) |
Figure S23. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.861 (Fisher's exact test), Q value = 1
Table S32. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 49 | 87 |
subtype1 | 6 | 10 |
subtype2 | 33 | 56 |
subtype3 | 10 | 21 |
Figure S24. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
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Cluster data file = SKCM-TM.mergedcluster.txt
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Clinical data file = SKCM-TP.clin.merged.picked.txt
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Number of patients = 144
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Number of clustering approaches = 8
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Number of selected clinical features = 3
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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
For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.