Correlation between aggregated molecular cancer subtypes and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1KW5F4C
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KW5F4C
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 5 clinical features across 179 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

- 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

- CNMF clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH'.

- 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

- 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.

- 5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

- 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 5 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 | YEARS TO BIRTH | GENDER | RACE | ETHNICITY
Statistical Tests | logrank test | Kruskal-Wallis (anova) | Fisher's exact test | Fisher's exact test | Fisher's exact test
Copy Number Ratio CNMF subtypes | 0.551 (1.00) | 0.813 (1.00) | 0.939 (1.00) | 0.418 (0.926) | 0.678 (1.00)
METHLYATION CNMF | 0.298 (0.926) | 0.107 (0.873) | 0.206 (0.873) | 0.21 (0.873) | 0.719 (1.00)
RPPA CNMF subtypes | 100 (1.00) | 0.591 (1.00) | 0.887 (1.00) | 0.257 (0.918) | 0.357 (0.926)
RPPA cHierClus subtypes | 100 (1.00) | 0.188 (0.873) | 0.134 (0.873) | 0.835 (1.00) | 1 (1.00)
RNAseq CNMF subtypes | 0.826 (1.00) | 0.459 (0.926) | 0.42 (0.926) | 0.238 (0.914) | 0.0568 (0.71)
RNAseq cHierClus subtypes | 0.814 (1.00) | 0.00266 (0.133) | 0.496 (0.954) | 0.773 (1.00) | 0.193 (0.873)
MIRSEQ CNMF | 0.17 (0.873) | 0.44 (0.926) | 1 (1.00) | 0.888 (1.00) | 0.358 (0.926)
MIRSEQ CHIERARCHICAL | 0.65 (1.00) | 0.918 (1.00) | 0.858 (1.00) | 0.667 (1.00) | 0.329 (0.926)
MIRseq Mature CNMF subtypes | 0.443 (0.926) | 0.0109 (0.273) | 0.61 (1.00) | 0.577 (1.00) | 0.437 (0.926)
MIRseq Mature cHierClus subtypes | 0.687 (1.00) | 0.0453 (0.71) | 0.463 (0.926) | 0.857 (1.00) | 0.114 (0.873)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 60 | 63 | 39

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.551 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 162 | 6 | 0.1 - 316.7 (21.4)
subtype1 | 60 | 3 | 0.7 - 316.7 (22.4)
subtype2 | 63 | 1 | 0.8 - 127.6 (25.3)
subtype3 | 39 | 2 | 0.1 - 137.6 (14.2)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.813 (Kruskal-Wallis (anova)), Q value = 1

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 162 | 47.5 (15.2)
subtype1 | 60 | 48.4 (15.3)
subtype2 | 63 | 46.5 (15.6)
subtype3 | 39 | 47.7 (15.0)

Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.939 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 88 | 74
subtype1 | 33 | 27
subtype2 | 33 | 30
subtype3 | 22 | 17

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.418 (Fisher's exact test), Q value = 0.93

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 5 | 19 | 134
subtype1 | 1 | 3 | 4 | 51
subtype2 | 0 | 2 | 10 | 50
subtype3 | 0 | 0 | 5 | 33

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.678 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 2 | 128
subtype1 | 0 | 51
subtype2 | 2 | 51
subtype3 | 0 | 26

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S7. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 45 | 85 | 49

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.298 (logrank test), Q value = 0.93

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 6 | 0.1 - 316.7 (20.6)
subtype1 | 45 | 3 | 0.2 - 316.7 (24.8)
subtype2 | 85 | 3 | 0.1 - 127.6 (18.3)
subtype3 | 49 | 0 | 0.7 - 121.0 (25.3)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.107 (Kruskal-Wallis (anova)), Q value = 0.87

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 47.3 (15.1)
subtype1 | 45 | 45.0 (15.0)
subtype2 | 85 | 50.0 (14.7)
subtype3 | 49 | 44.9 (15.5)

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.206 (Fisher's exact test), Q value = 0.87

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 78
subtype1 | 21 | 24
subtype2 | 48 | 37
subtype3 | 32 | 17

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.21 (Fisher's exact test), Q value = 0.87

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 148
subtype1 | 0 | 1 | 7 | 36
subtype2 | 1 | 2 | 5 | 74
subtype3 | 0 | 3 | 8 | 38

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.719 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 138
subtype1 | 2 | 34
subtype2 | 2 | 63
subtype3 | 1 | 41

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S13. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6
--- | --- | --- | --- | --- | --- | ---
Number of samples | 14 | 9 | 20 | 6 | 17 | 13

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S14. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 2 | 0.7 - 137.6 (23.8)
subtype1 | 14 | 0 | 0.8 - 78.2 (23.4)
subtype2 | 9 | 1 | 0.9 - 137.6 (25.3)
subtype3 | 20 | 0 | 0.7 - 81.7 (21.6)
subtype4 | 6 | 0 | 2.9 - 72.4 (15.4)
subtype5 | 17 | 1 | 2.9 - 108.3 (28.1)
subtype6 | 13 | 0 | 1.8 - 127.6 (20.1)

Figure S11. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.591 (Kruskal-Wallis (anova)), Q value = 1

Table S15. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 47.8 (14.7)
subtype1 | 14 | 51.9 (16.2)
subtype2 | 9 | 50.2 (8.2)
subtype3 | 20 | 50.5 (14.5)
subtype4 | 6 | 43.0 (16.7)
subtype5 | 17 | 44.5 (11.8)
subtype6 | 13 | 43.8 (18.9)

Figure S12. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.887 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 40 | 39
subtype1 | 8 | 6
subtype2 | 4 | 5
subtype3 | 11 | 9
subtype4 | 4 | 2
subtype5 | 7 | 10
subtype6 | 6 | 7

Figure S13. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.257 (Fisher's exact test), Q value = 0.92

Table S17. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 4 | 9 | 65
subtype1 | 2 | 0 | 11
subtype2 | 0 | 0 | 9
subtype3 | 0 | 4 | 16
subtype4 | 1 | 1 | 4
subtype5 | 1 | 3 | 13
subtype6 | 0 | 1 | 12

Figure S14. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.357 (Fisher's exact test), Q value = 0.93

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 58
subtype1 | 1 | 9
subtype2 | 0 | 7
subtype3 | 0 | 16
subtype4 | 0 | 4
subtype5 | 0 | 11
subtype6 | 0 | 11

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S19. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 18 | 28

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S20. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 2 | 0.7 - 137.6 (23.8)
subtype1 | 33 | 2 | 0.9 - 137.6 (28.1)
subtype2 | 18 | 0 | 0.8 - 81.7 (20.0)
subtype3 | 28 | 0 | 0.7 - 72.8 (17.7)

Figure S16. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.188 (Kruskal-Wallis (anova)), Q value = 0.87

Table S21. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 47.8 (14.7)
subtype1 | 33 | 50.9 (13.6)
subtype2 | 18 | 48.2 (12.9)
subtype3 | 28 | 43.8 (16.4)

Figure S17. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.134 (Fisher's exact test), Q value = 0.87

Table S22. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 40 | 39
subtype1 | 20 | 13
subtype2 | 10 | 8
subtype3 | 10 | 18

Figure S18. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.835 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 4 | 9 | 65
subtype1 | 1 | 4 | 28
subtype2 | 1 | 1 | 16
subtype3 | 2 | 4 | 21

Figure S19. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 58
subtype1 | 1 | 24
subtype2 | 0 | 15
subtype3 | 0 | 19

Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S25. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 39 | 69 | 50 | 21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.826 (logrank test), Q value = 1

Table S26. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 6 | 0.1 - 316.7 (20.6)
subtype1 | 39 | 2 | 0.1 - 316.7 (17.9)
subtype2 | 69 | 2 | 0.1 - 114.8 (20.1)
subtype3 | 50 | 2 | 0.8 - 137.6 (21.0)
subtype4 | 21 | 0 | 0.8 - 111.2 (27.9)

Figure S21. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.459 (Kruskal-Wallis (anova)), Q value = 0.93

Table S27. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 47.3 (15.1)
subtype1 | 39 | 45.1 (15.2)
subtype2 | 69 | 49.4 (14.9)
subtype3 | 50 | 45.7 (15.4)
subtype4 | 21 | 48.5 (15.1)

Figure S22. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.42 (Fisher's exact test), Q value = 0.93

Table S28. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 78
subtype1 | 25 | 14
subtype2 | 35 | 34
subtype3 | 27 | 23
subtype4 | 14 | 7

Figure S23. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.238 (Fisher's exact test), Q value = 0.91

Table S29. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 148
subtype1 | 0 | 1 | 7 | 28
subtype2 | 1 | 2 | 3 | 62
subtype3 | 0 | 2 | 6 | 42
subtype4 | 0 | 1 | 4 | 16

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0568 (Fisher's exact test), Q value = 0.71

Table S30. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 138
subtype1 | 2 | 26
subtype2 | 1 | 56
subtype3 | 0 | 40
subtype4 | 2 | 16

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S31. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 39 | 71 | 48 | 21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.814 (logrank test), Q value = 1

Table S32. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 6 | 0.1 - 316.7 (20.6)
subtype1 | 39 | 2 | 0.8 - 127.6 (26.8)
subtype2 | 71 | 2 | 0.1 - 114.8 (18.3)
subtype3 | 48 | 2 | 0.2 - 316.7 (18.4)
subtype4 | 21 | 0 | 0.8 - 111.2 (27.9)

Figure S26. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00266 (Kruskal-Wallis (anova)), Q value = 0.13

Table S33. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 47.3 (15.1)
subtype1 | 39 | 51.9 (12.6)
subtype2 | 71 | 49.3 (14.8)
subtype3 | 48 | 40.8 (15.6)
subtype4 | 21 | 47.1 (15.3)

Figure S27. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.496 (Fisher's exact test), Q value = 0.95

Table S34. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 78
subtype1 | 23 | 16
subtype2 | 41 | 30
subtype3 | 23 | 25
subtype4 | 14 | 7

Figure S28. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.773 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 148
subtype1 | 0 | 2 | 4 | 33
subtype2 | 1 | 2 | 5 | 60
subtype3 | 0 | 1 | 8 | 38
subtype4 | 0 | 1 | 3 | 17

Figure S29. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.193 (Fisher's exact test), Q value = 0.87

Table S36. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 138
subtype1 | 1 | 29
subtype2 | 2 | 55
subtype3 | 0 | 38
subtype4 | 2 | 16

Figure S30. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S37. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 75 | 65 | 39

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.17 (logrank test), Q value = 0.87

Table S38. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 6 | 0.1 - 316.7 (20.6)
subtype1 | 75 | 5 | 0.2 - 316.7 (24.2)
subtype2 | 65 | 0 | 0.1 - 114.8 (19.9)
subtype3 | 39 | 1 | 0.7 - 137.6 (23.1)

Figure S31. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.44 (Kruskal-Wallis (anova)), Q value = 0.93

Table S39. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 47.3 (15.1)
subtype1 | 75 | 45.5 (16.0)
subtype2 | 65 | 48.2 (14.7)
subtype3 | 39 | 49.4 (14.1)

Figure S32. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 1 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 78
subtype1 | 42 | 33
subtype2 | 37 | 28
subtype3 | 22 | 17

Figure S33. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.888 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 148
subtype1 | 1 | 3 | 9 | 61
subtype2 | 0 | 2 | 5 | 55
subtype3 | 0 | 1 | 6 | 32

Figure S34. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.358 (Fisher's exact test), Q value = 0.93

Table S42. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 138
subtype1 | 1 | 60
subtype2 | 2 | 50
subtype3 | 2 | 28

Figure S35. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S43. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 77 | 59 | 18 | 25

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.65 (logrank test), Q value = 1

Table S44. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 6 | 0.1 - 316.7 (20.6)
subtype1 | 77 | 4 | 0.2 - 316.7 (19.6)
subtype2 | 59 | 2 | 0.1 - 114.8 (24.4)
subtype3 | 18 | 0 | 0.7 - 86.9 (27.5)
subtype4 | 25 | 0 | 0.8 - 111.2 (19.9)

Figure S36. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.918 (Kruskal-Wallis (anova)), Q value = 1

Table S45. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 47.3 (15.1)
subtype1 | 77 | 46.4 (15.7)
subtype2 | 59 | 48.6 (15.3)
subtype3 | 18 | 47.2 (13.0)
subtype4 | 25 | 47.5 (14.9)

Figure S37. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.858 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 78
subtype1 | 42 | 35
subtype2 | 35 | 24
subtype3 | 9 | 9
subtype4 | 15 | 10

Figure S38. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.667 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 148
subtype1 | 0 | 3 | 10 | 63
subtype2 | 1 | 2 | 4 | 49
subtype3 | 0 | 0 | 1 | 17
subtype4 | 0 | 1 | 5 | 19

Figure S39. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.329 (Fisher's exact test), Q value = 0.93

Table S48. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 138
subtype1 | 1 | 61
subtype2 | 2 | 45
subtype3 | 0 | 12
subtype4 | 2 | 20

Figure S40. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S49. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 36 | 64 | 12 | 42 | 24

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.443 (logrank test), Q value = 0.93

Table S50. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 6 | 0.1 - 316.7 (20.4)
subtype1 | 36 | 0 | 0.7 - 127.6 (25.5)
subtype2 | 64 | 2 | 0.1 - 114.8 (19.8)
subtype3 | 12 | 1 | 1.2 - 116.2 (10.5)
subtype4 | 42 | 2 | 0.2 - 121.0 (22.5)
subtype5 | 24 | 1 | 0.9 - 316.7 (26.5)

Figure S41. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0109 (Kruskal-Wallis (anova)), Q value = 0.27

Table S51. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 178 | 47.2 (15.1)
subtype1 | 36 | 51.1 (13.3)
subtype2 | 64 | 48.9 (14.6)
subtype3 | 12 | 53.8 (13.0)
subtype4 | 42 | 42.1 (16.2)
subtype5 | 24 | 42.8 (15.3)

Figure S42. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.61 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 77
subtype1 | 24 | 12
subtype2 | 35 | 29
subtype3 | 7 | 5
subtype4 | 24 | 18
subtype5 | 11 | 13

Figure S43. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.577 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 147
subtype1 | 0 | 0 | 5 | 31
subtype2 | 0 | 3 | 5 | 54
subtype3 | 1 | 0 | 1 | 9
subtype4 | 0 | 2 | 6 | 33
subtype5 | 0 | 1 | 3 | 20

Figure S44. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.437 (Fisher's exact test), Q value = 0.93

Table S54. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 137
subtype1 | 1 | 25
subtype2 | 2 | 52
subtype3 | 0 | 8
subtype4 | 0 | 33
subtype5 | 2 | 19

Figure S45. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S55. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 80 | 61 | 18 | 19

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.687 (logrank test), Q value = 1

Table S56. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 6 | 0.1 - 316.7 (20.4)
subtype1 | 80 | 3 | 0.1 - 114.8 (19.7)
subtype2 | 61 | 3 | 0.2 - 316.7 (25.3)
subtype3 | 18 | 0 | 0.7 - 86.9 (27.5)
subtype4 | 19 | 0 | 0.8 - 111.2 (20.6)

Figure S46. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0453 (Kruskal-Wallis (anova)), Q value = 0.71

Table S57. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 178 | 47.2 (15.1)
subtype1 | 80 | 50.3 (14.6)
subtype2 | 61 | 43.0 (15.5)
subtype3 | 18 | 47.2 (13.0)
subtype4 | 19 | 48.2 (15.7)

Figure S47. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.463 (Fisher's exact test), Q value = 0.93

Table S58. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 77
subtype1 | 48 | 32
subtype2 | 31 | 30
subtype3 | 9 | 9
subtype4 | 13 | 6

Figure S48. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.857 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 147
subtype1 | 1 | 3 | 7 | 66
subtype2 | 0 | 2 | 9 | 49
subtype3 | 0 | 0 | 1 | 17
subtype4 | 0 | 1 | 3 | 15

Figure S49. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.114 (Fisher's exact test), Q value = 0.87

Table S60. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 5 | 137
subtype1 | 3 | 62
subtype2 | 0 | 49
subtype3 | 0 | 12
subtype4 | 2 | 14

Figure S50. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Methods & Data
Input

- Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PCPG-TP/15107813/PCPG-TP.mergedcluster.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PCPG-TP/15087180/PCPG-TP.merged_data.txt

- Number of patients = 179

- Number of clustering approaches = 10

- Number of selected clinical features = 5

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

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

Fisher's exact test

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

Q value calculation

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle