Correlation between aggregated molecular cancer subtypes and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1W37VBN
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/C1W37VBN
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 6 different clustering approaches and 5 clinical features across 200 patients, 9 significant findings 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 correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.

- 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'RACE'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.

- 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.

- 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. 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 6 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, 9 significant findings 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 | 0.00947 (0.0523) | 0.0454 (0.157) | 0.0762 (0.228) | 1 (1.00) | 0.264 (0.494)
METHLYATION CNMF | 0.000606 (0.00454) | 5.28e-07 (1.59e-05) | 0.503 (0.686) | 0.441 (0.652) | 0.674 (0.843)
RNAseq CNMF subtypes | 0.396 (0.652) | 0.24 (0.48) | 0.0973 (0.243) | 0.0376 (0.157) | 0.583 (0.76)
RNAseq cHierClus subtypes | 0.000473 (0.00454) | 3.26e-05 (0.00049) | 0.416 (0.652) | 0.144 (0.333) | 0.0835 (0.228)
MIRSEQ CNMF | 0.0472 (0.157) | 0.0105 (0.0523) | 0.92 (1.00) | 0.862 (1.00) | 1 (1.00)
MIRSEQ CHIERARCHICAL | 0.161 (0.344) | 0.457 (0.652) | 0.388 (0.652) | 0.956 (1.00) | 1 (1.00)

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 | 4
--- | --- | --- | --- | ---
Number of samples | 148 | 14 | 27 | 2

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

P value = 0.00947 (logrank test), Q value = 0.052

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 176 | 115 | 0.0 - 94.1 (11.0)
subtype1 | 139 | 85 | 0.0 - 94.1 (12.0)
subtype2 | 12 | 9 | 0.0 - 42.0 (9.5)
subtype3 | 25 | 21 | 0.0 - 73.0 (9.0)

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 = 0.0454 (Kruskal-Wallis (anova)), Q value = 0.16

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 189 | 55.1 (16.1)
subtype1 | 148 | 53.7 (16.0)
subtype2 | 14 | 62.4 (13.1)
subtype3 | 27 | 58.7 (16.6)

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 = 0.0762 (Fisher's exact test), Q value = 0.23

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 103
subtype1 | 73 | 75
subtype2 | 6 | 8
subtype3 | 7 | 20

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

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

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 14 | 171
subtype1 | 2 | 11 | 133
subtype2 | 0 | 1 | 13
subtype3 | 0 | 2 | 25

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 = 0.264 (Fisher's exact test), Q value = 0.49

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

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 3 | 183
subtype1 | 2 | 143
subtype2 | 1 | 13
subtype3 | 0 | 27

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 | 4
--- | --- | --- | --- | ---
Number of samples | 34 | 63 | 50 | 47

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000606 (logrank test), Q value = 0.0045

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 116 | 0.0 - 94.1 (12.0)
subtype1 | 32 | 12 | 0.0 - 84.1 (21.0)
subtype2 | 57 | 46 | 0.0 - 56.1 (10.1)
subtype3 | 48 | 28 | 0.0 - 94.1 (11.0)
subtype4 | 42 | 30 | 0.0 - 55.0 (9.5)

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 = 5.28e-07 (Kruskal-Wallis (anova)), Q value = 1.6e-05

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 194 | 55.1 (16.0)
subtype1 | 34 | 46.6 (14.9)
subtype2 | 63 | 63.2 (12.8)
subtype3 | 50 | 50.2 (16.7)
subtype4 | 47 | 55.6 (15.1)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.503 (Fisher's exact test), Q value = 0.69

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 89 | 105
subtype1 | 19 | 15
subtype2 | 26 | 37
subtype3 | 21 | 29
subtype4 | 23 | 24

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.441 (Fisher's exact test), Q value = 0.65

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

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 13 | 177
subtype1 | 0 | 2 | 31
subtype2 | 0 | 5 | 58
subtype3 | 1 | 1 | 47
subtype4 | 1 | 5 | 41

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.674 (Fisher's exact test), Q value = 0.84

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

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 190
subtype1 | 0 | 33
subtype2 | 0 | 62
subtype3 | 1 | 48
subtype4 | 0 | 47

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 74 | 54 | 45

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.396 (logrank test), Q value = 0.65

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 160 | 103 | 0.0 - 94.1 (11.5)
subtype1 | 68 | 42 | 0.0 - 94.1 (14.5)
subtype2 | 52 | 35 | 0.0 - 75.1 (9.0)
subtype3 | 40 | 26 | 0.0 - 62.0 (11.0)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.24 (Kruskal-Wallis (anova)), Q value = 0.48

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 173 | 55.3 (16.1)
subtype1 | 74 | 54.4 (17.3)
subtype2 | 54 | 58.4 (13.9)
subtype3 | 45 | 52.9 (16.4)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0973 (Fisher's exact test), Q value = 0.24

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 80 | 93
subtype1 | 31 | 43
subtype2 | 22 | 32
subtype3 | 27 | 18

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0376 (Fisher's exact test), Q value = 0.16

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

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 13 | 156
subtype1 | 1 | 3 | 68
subtype2 | 1 | 2 | 51
subtype3 | 0 | 8 | 37

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.583 (Fisher's exact test), Q value = 0.76

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

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 169
subtype1 | 0 | 71
subtype2 | 1 | 53
subtype3 | 0 | 45

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6
--- | --- | --- | --- | --- | --- | ---
Number of samples | 16 | 14 | 15 | 58 | 26 | 44

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000473 (logrank test), Q value = 0.0045

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 160 | 103 | 0.0 - 94.1 (11.5)
subtype1 | 16 | 5 | 0.0 - 62.0 (36.0)
subtype2 | 14 | 5 | 0.9 - 75.1 (20.0)
subtype3 | 12 | 5 | 6.0 - 94.1 (20.5)
subtype4 | 53 | 41 | 0.0 - 73.0 (10.1)
subtype5 | 25 | 15 | 0.0 - 62.0 (8.1)
subtype6 | 40 | 32 | 0.0 - 69.0 (8.5)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.26e-05 (Kruskal-Wallis (anova)), Q value = 0.00049

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 173 | 55.3 (16.1)
subtype1 | 16 | 47.8 (14.9)
subtype2 | 14 | 57.7 (14.6)
subtype3 | 15 | 36.6 (13.9)
subtype4 | 58 | 59.8 (15.5)
subtype5 | 26 | 53.3 (16.7)
subtype6 | 44 | 58.7 (13.0)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.416 (Fisher's exact test), Q value = 0.65

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 80 | 93
subtype1 | 9 | 7
subtype2 | 6 | 8
subtype3 | 6 | 9
subtype4 | 22 | 36
subtype5 | 16 | 10
subtype6 | 21 | 23

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.144 (Fisher's exact test), Q value = 0.33

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

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 13 | 156
subtype1 | 0 | 2 | 13
subtype2 | 0 | 0 | 14
subtype3 | 0 | 0 | 15
subtype4 | 1 | 3 | 53
subtype5 | 0 | 6 | 20
subtype6 | 1 | 2 | 41

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0835 (Fisher's exact test), Q value = 0.23

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

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 169
subtype1 | 0 | 15
subtype2 | 1 | 13
subtype3 | 0 | 15
subtype4 | 0 | 56
subtype5 | 0 | 26
subtype6 | 0 | 44

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 60 | 38 | 43 | 47

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0472 (logrank test), Q value = 0.16

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 174 | 112 | 0.0 - 94.1 (12.0)
subtype1 | 58 | 43 | 0.0 - 69.0 (8.1)
subtype2 | 36 | 20 | 0.0 - 62.0 (13.5)
subtype3 | 39 | 27 | 0.0 - 94.1 (11.0)
subtype4 | 41 | 22 | 0.0 - 73.0 (16.0)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0105 (Kruskal-Wallis (anova)), Q value = 0.052

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 188 | 54.9 (16.2)
subtype1 | 60 | 57.4 (14.3)
subtype2 | 38 | 56.6 (14.9)
subtype3 | 43 | 57.4 (18.0)
subtype4 | 47 | 48.0 (16.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.92 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 87 | 101
subtype1 | 26 | 34
subtype2 | 17 | 21
subtype3 | 21 | 22
subtype4 | 23 | 24

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.862 (Fisher's exact test), Q value = 1

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

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 13 | 171
subtype1 | 1 | 4 | 55
subtype2 | 0 | 2 | 36
subtype3 | 0 | 2 | 40
subtype4 | 1 | 5 | 40

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

'MIRSEQ CNMF' versus 'ETHNICITY'

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

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

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 184
subtype1 | 1 | 59
subtype2 | 0 | 38
subtype3 | 0 | 41
subtype4 | 0 | 46

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S31. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 70 | 35 | 83

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.161 (logrank test), Q value = 0.34

Table S32. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 174 | 112 | 0.0 - 94.1 (12.0)
subtype1 | 68 | 48 | 0.0 - 69.0 (8.1)
subtype2 | 33 | 19 | 0.0 - 62.0 (12.0)
subtype3 | 73 | 45 | 0.0 - 94.1 (12.9)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.457 (Kruskal-Wallis (anova)), Q value = 0.65

Table S33. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 188 | 54.9 (16.2)
subtype1 | 70 | 55.8 (14.8)
subtype2 | 35 | 57.3 (15.2)
subtype3 | 83 | 53.1 (17.6)

Figure S27. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.388 (Fisher's exact test), Q value = 0.65

Table S34. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 87 | 101
subtype1 | 36 | 34
subtype2 | 13 | 22
subtype3 | 38 | 45

Figure S28. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.956 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 2 | 13 | 171
subtype1 | 1 | 6 | 63
subtype2 | 0 | 2 | 33
subtype3 | 1 | 5 | 75

Figure S29. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S36. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 1 | 184
subtype1 | 0 | 70
subtype2 | 0 | 35
subtype3 | 1 | 79

Figure S30. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

Methods & Data
Input

- Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LAML-TB/15098833/LAML-TB.mergedcluster.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/15082450/LAML-TB.merged_data.txt

- Number of patients = 200

- Number of clustering approaches = 6

- 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)
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