Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Chromophobe (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1SX6BHN
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1SX6BHN
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 8 different clustering approaches and 10 clinical features across 64 patients, no 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 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 5 subtypes that do not correlate to any clinical features.

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

- 2 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 8 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical Features | Statistical Tests | Copy Number Ratio CNMF subtypes | METHLYATION CNMF | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRseq Mature CNMF subtypes | MIRseq Mature cHierClus subtypes
Time to Death | logrank test | 0.329 (1.00) | 0.534 (1.00) | 0.179 (1.00) | 0.0507 (1.00) | 0.168 (1.00) | 0.0756 (1.00) | 0.249 (1.00) | 0.693 (1.00)
AGE | ANOVA | 0.894 (1.00) | 0.442 (1.00) | 0.345 (1.00) | 0.271 (1.00) | 0.00506 (0.349) | 0.159 (1.00) | 0.403 (1.00) | 0.052 (1.00)
NEOPLASM DISEASESTAGE | Chi-square test | 0.493 (1.00) | 0.0681 (1.00) | 0.217 (1.00) | 0.278 (1.00) | 0.18 (1.00) | 0.254 (1.00) | 0.163 (1.00) | 0.61 (1.00)
PATHOLOGY T STAGE | Chi-square test | 0.489 (1.00) | 0.0308 (1.00) | 0.108 (1.00) | 0.197 (1.00) | 0.142 (1.00) | 0.245 (1.00) | 0.0974 (1.00) | 0.455 (1.00)
PATHOLOGY N STAGE | Fisher's exact test | 0.565 (1.00) | 0.383 (1.00) | 0.113 (1.00) | 0.105 (1.00) | 0.722 (1.00) | 0.387 (1.00) | 0.56 (1.00) | 1 (1.00)
PATHOLOGY M STAGE | Chi-square test | 0.786 (1.00) | 0.22 (1.00) | 0.188 (1.00) | 0.0973 (1.00) | 0.292 (1.00) | 0.071 (1.00) | 0.363 (1.00) | 0.128 (1.00)
GENDER | Fisher's exact test | 0.535 (1.00) | 0.838 (1.00) | 0.487 (1.00) | 0.654 (1.00) | 0.654 (1.00) | 0.463 (1.00) | 0.194 (1.00) | 0.242 (1.00)
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.0658 (1.00) | 0.608 (1.00) | 0.264 (1.00) | 0.264 (1.00) | 0.0288 (1.00) | 0.148 (1.00) | 0.0859 (1.00) | 0.142 (1.00)
NUMBERPACKYEARSSMOKED | ANOVA |  | 0.088 (1.00) | 0.105 (1.00) | 0.105 (1.00) | 0.497 (1.00) |  |  | 0.497 (1.00)
YEAROFTOBACCOSMOKINGONSET | ANOVA |  |  |  |  |  |  |  |

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 | 5 | 45 | 14

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

P value = 0.329 (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 | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 5 | 1 | 32.7 - 92.7 (38.1)
subtype2 | 44 | 6 | 0.6 - 151.9 (63.8)
subtype3 | 14 | 0 | 2.5 - 137.1 (68.3)

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 'AGE'

P value = 0.894 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 5 | 51.6 (11.7)
subtype2 | 45 | 51.7 (14.8)
subtype3 | 14 | 49.6 (14.5)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.493 (Chi-square test), Q value = 1

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

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 1 | 2 | 2 | 0
subtype2 | 13 | 16 | 10 | 6
subtype3 | 7 | 5 | 2 | 0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.489 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 1 | 2 | 2
subtype2 | 13 | 16 | 16
subtype3 | 7 | 5 | 2

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.565 (Fisher's exact test), Q value = 1

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

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 2 | 0
subtype2 | 30 | 5
subtype3 | 8 | 0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.786 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 3 | 0 | 1
subtype2 | 21 | 2 | 7
subtype3 | 8 | 0 | 1

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.535 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 3 | 2
subtype2 | 16 | 29
subtype3 | 6 | 8

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0658 (ANOVA), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 1 | 90.0 (NA)
subtype2 | 7 | 85.7 (9.8)
subtype3 | 3 | 96.7 (5.8)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 17 | 34 | 13

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.534 (logrank test), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 17 | 1 | 23.5 - 137.1 (72.7)
subtype2 | 33 | 5 | 2.5 - 151.9 (65.2)
subtype3 | 13 | 1 | 0.6 - 98.3 (51.0)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.442 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 17 | 49.3 (14.0)
subtype2 | 34 | 50.4 (13.9)
subtype3 | 13 | 55.7 (16.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0681 (Chi-square test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 8 | 6 | 3 | 0
subtype2 | 6 | 16 | 8 | 4
subtype3 | 7 | 1 | 3 | 2

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0308 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 8 | 6 | 3
subtype2 | 6 | 16 | 12
subtype3 | 7 | 1 | 5

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.383 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 12 | 0
subtype2 | 23 | 4
subtype3 | 5 | 1

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.22 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 11 | 0 | 2
subtype2 | 16 | 2 | 3
subtype3 | 5 | 0 | 4

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.838 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 7 | 10
subtype2 | 14 | 20
subtype3 | 4 | 9

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.608 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 3 | 93.3 (5.8)
subtype2 | 3 | 90.0 (10.0)
subtype3 | 5 | 86.0 (11.4)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.088 (ANOVA), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 27.5 (21.5)
subtype1 | 3 | 12.0 (13.1)
subtype2 | 5 | 39.0 (24.4)
subtype3 | 2 | 22.0 (2.8)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S20. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 19 | 20 | 15 | 10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.179 (logrank test), Q value = 1

Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 19 | 2 | 24.8 - 137.1 (76.9)
subtype2 | 19 | 0 | 2.5 - 151.9 (62.3)
subtype3 | 15 | 4 | 16.7 - 114.2 (66.5)
subtype4 | 10 | 1 | 0.6 - 98.3 (30.1)

Figure S18. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.345 (ANOVA), Q value = 1

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 19 | 47.7 (12.9)
subtype2 | 20 | 50.8 (15.4)
subtype3 | 15 | 51.6 (14.3)
subtype4 | 10 | 58.0 (14.6)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.217 (Chi-square test), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 7 | 6 | 5 | 1
subtype2 | 8 | 9 | 2 | 1
subtype3 | 1 | 7 | 5 | 2
subtype4 | 5 | 1 | 2 | 2

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.108 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 7 | 6 | 6
subtype2 | 8 | 9 | 3
subtype3 | 1 | 7 | 7
subtype4 | 5 | 1 | 4

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.113 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 12 | 1
subtype2 | 15 | 0
subtype3 | 10 | 3
subtype4 | 3 | 1

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.188 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 13 | 0 | 2
subtype2 | 9 | 1 | 2
subtype3 | 7 | 1 | 1
subtype4 | 3 | 0 | 4

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.487 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 9 | 10
subtype2 | 9 | 11
subtype3 | 5 | 10
subtype4 | 2 | 8

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.264 (ANOVA), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 3 | 93.3 (5.8)
subtype2 | 3 | 93.3 (11.5)
subtype3 | 1 | 90.0 (NA)
subtype4 | 4 | 82.5 (9.6)

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 27.5 (21.5)
subtype1 | 3 | 14.3 (11.2)
subtype2 | 3 | 20.7 (16.3)
subtype3 | 3 | 50.0 (24.1)
subtype4 | 1 | 20.0 (NA)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S30. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 10 | 10 | 16 | 6 | 22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0507 (logrank test), Q value = 1

Table S31. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 10 | 4 | 16.7 - 114.2 (60.0)
subtype2 | 10 | 1 | 0.6 - 98.3 (26.1)
subtype3 | 15 | 0 | 3.5 - 151.9 (72.7)
subtype4 | 6 | 0 | 2.5 - 109.2 (52.3)
subtype5 | 22 | 2 | 24.8 - 137.1 (75.4)

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.271 (ANOVA), Q value = 1

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 10 | 53.6 (12.7)
subtype2 | 10 | 55.1 (14.4)
subtype3 | 16 | 48.1 (14.1)
subtype4 | 6 | 60.3 (21.1)
subtype5 | 22 | 48.1 (12.8)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.278 (Chi-square test), Q value = 1

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 1 | 3 | 4 | 2
subtype2 | 5 | 2 | 1 | 2
subtype3 | 7 | 6 | 2 | 1
subtype4 | 0 | 4 | 2 | 0
subtype5 | 8 | 8 | 5 | 1

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.197 (Chi-square test), Q value = 1

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 1 | 3 | 6
subtype2 | 5 | 2 | 3
subtype3 | 7 | 6 | 3
subtype4 | 0 | 4 | 2
subtype5 | 8 | 8 | 6

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.105 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 6 | 3
subtype2 | 3 | 1
subtype3 | 12 | 0
subtype4 | 4 | 0
subtype5 | 15 | 1

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0973 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 4 | 1 | 1
subtype2 | 2 | 0 | 4
subtype3 | 7 | 1 | 2
subtype4 | 3 | 0 | 0
subtype5 | 16 | 0 | 2

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.654 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 4 | 6
subtype2 | 2 | 8
subtype3 | 8 | 8
subtype4 | 2 | 4
subtype5 | 9 | 13

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.264 (ANOVA), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 1 | 90.0 (NA)
subtype2 | 4 | 82.5 (9.6)
subtype3 | 3 | 93.3 (11.5)
subtype5 | 3 | 93.3 (5.8)

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 27.5 (21.5)
subtype1 | 3 | 50.0 (24.1)
subtype2 | 1 | 20.0 (NA)
subtype3 | 3 | 20.7 (16.3)
subtype5 | 3 | 14.3 (11.2)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S40. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 20 | 23 | 21

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.168 (logrank test), Q value = 1

Table S41. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 19 | 4 | 0.6 - 151.9 (51.0)
subtype2 | 23 | 2 | 3.5 - 137.1 (79.0)
subtype3 | 21 | 1 | 11.2 - 114.2 (63.9)

Figure S36. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00506 (ANOVA), Q value = 0.35

Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 20 | 58.2 (15.9)
subtype2 | 23 | 51.6 (12.9)
subtype3 | 21 | 44.0 (11.1)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.18 (Chi-square test), Q value = 1

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 6 | 4 | 7 | 3
subtype2 | 9 | 8 | 3 | 3
subtype3 | 6 | 11 | 4 | 0

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.142 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 6 | 4 | 10
subtype2 | 9 | 8 | 6
subtype3 | 6 | 11 | 4

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.722 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 10 | 2
subtype2 | 14 | 2
subtype3 | 16 | 1

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.292 (Chi-square test), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 8 | 1 | 5
subtype2 | 14 | 1 | 1
subtype3 | 10 | 0 | 3

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.654 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 6 | 14
subtype2 | 10 | 13
subtype3 | 9 | 12

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0288 (ANOVA), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 5 | 82.0 (8.4)
subtype2 | 2 | 95.0 (7.1)
subtype3 | 4 | 95.0 (5.8)

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.497 (ANOVA), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 27.5 (21.5)
subtype1 | 3 | 31.7 (14.6)
subtype2 | 3 | 14.3 (11.2)
subtype3 | 4 | 34.2 (30.2)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S50. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 9 | 55

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0756 (logrank test), Q value = 1

Table S51. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 9 | 2 | 0.6 - 75.7 (28.8)
subtype2 | 54 | 5 | 2.5 - 151.9 (70.3)

Figure S45. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.159 (t-test), Q value = 1

Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 9 | 58.4 (15.6)
subtype2 | 55 | 50.0 (14.0)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.254 (Chi-square test), Q value = 1

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 4 | 1 | 2 | 2
subtype2 | 17 | 22 | 12 | 4

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.245 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 4 | 1 | 4
subtype2 | 17 | 22 | 16

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.387 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 3 | 1
subtype2 | 37 | 4

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.071 (Chi-square test), Q value = 1

Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 2 | 0 | 3
subtype2 | 30 | 2 | 6

Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.463 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 2 | 7
subtype2 | 23 | 32

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.148 (t-test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 3 | 80.0 (10.0)
subtype2 | 8 | 92.5 (7.1)

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S59. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 10 | 15 | 15 | 10 | 14

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

P value = 0.249 (logrank test), Q value = 1

Table S60. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 10 | 2 | 3.5 - 97.0 (63.4)
subtype2 | 15 | 0 | 2.5 - 123.1 (51.0)
subtype3 | 15 | 2 | 24.8 - 137.1 (79.0)
subtype4 | 10 | 0 | 11.2 - 86.1 (67.2)
subtype5 | 13 | 3 | 0.6 - 151.9 (52.3)

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.403 (ANOVA), Q value = 1

Table S61. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 10 | 50.2 (10.7)
subtype2 | 15 | 51.1 (15.9)
subtype3 | 15 | 48.6 (16.1)
subtype4 | 10 | 47.3 (12.1)
subtype5 | 14 | 57.6 (14.3)

Figure S54. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.163 (Chi-square test), Q value = 1

Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 5 | 0 | 3 | 2
subtype2 | 6 | 7 | 2 | 0
subtype3 | 4 | 8 | 1 | 2
subtype4 | 2 | 5 | 3 | 0
subtype5 | 4 | 3 | 5 | 2

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0974 (Chi-square test), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 5 | 0 | 5
subtype2 | 6 | 7 | 2
subtype3 | 4 | 8 | 3
subtype4 | 2 | 5 | 3
subtype5 | 4 | 3 | 7

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.56 (Chi-square test), Q value = 1

Table S64. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 6 | 1
subtype2 | 9 | 0
subtype3 | 11 | 2
subtype4 | 6 | 0
subtype5 | 8 | 2

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.363 (Chi-square test), Q value = 1

Table S65. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 7 | 1 | 0
subtype2 | 4 | 0 | 3
subtype3 | 11 | 1 | 1
subtype4 | 5 | 0 | 2
subtype5 | 5 | 0 | 3

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.194 (Chi-square test), Q value = 1

Table S66. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 7 | 3
subtype2 | 6 | 9
subtype3 | 5 | 10
subtype4 | 4 | 6
subtype5 | 3 | 11

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0859 (ANOVA), Q value = 1

Table S67. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype2 | 3 | 86.7 (5.8)
subtype3 | 2 | 95.0 (7.1)
subtype4 | 3 | 96.7 (5.8)
subtype5 | 3 | 80.0 (10.0)

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S68. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 12 | 7 | 24 | 21

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

P value = 0.693 (logrank test), Q value = 1

Table S69. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
subtype1 | 12 | 2 | 0.6 - 98.3 (41.8)
subtype2 | 7 | 0 | 2.5 - 102.8 (25.9)
subtype3 | 23 | 2 | 11.2 - 151.9 (63.9)
subtype4 | 21 | 3 | 24.8 - 137.1 (76.9)

Figure S61. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.052 (ANOVA), Q value = 1

Table S70. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 51.2 (14.4)
subtype1 | 12 | 57.6 (16.5)
subtype2 | 7 | 60.4 (15.3)
subtype3 | 24 | 47.2 (13.7)
subtype4 | 21 | 49.0 (11.8)

Figure S62. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.61 (Chi-square test), Q value = 1

Table S71. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV
--- | --- | --- | --- | ---
ALL | 21 | 23 | 14 | 6
subtype1 | 4 | 2 | 4 | 2
subtype2 | 2 | 4 | 0 | 1
subtype3 | 7 | 11 | 5 | 1
subtype4 | 8 | 6 | 5 | 2

Figure S63. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.455 (Chi-square test), Q value = 1

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 21 | 23 | 20
subtype1 | 4 | 2 | 6
subtype2 | 2 | 4 | 1
subtype3 | 7 | 11 | 6
subtype4 | 8 | 6 | 7

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

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

Table S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | N0 | N1+N2
--- | --- | ---
ALL | 40 | 5
subtype1 | 6 | 1
subtype2 | 4 | 0
subtype3 | 17 | 2
subtype4 | 13 | 2

Figure S65. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.128 (Chi-square test), Q value = 1

Table S74. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 32 | 2 | 9
subtype1 | 5 | 0 | 3
subtype2 | 2 | 1 | 0
subtype3 | 10 | 1 | 4
subtype4 | 15 | 0 | 2

Figure S66. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.242 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 25 | 39
subtype1 | 2 | 10
subtype2 | 3 | 4
subtype3 | 9 | 15
subtype4 | 11 | 10

Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.142 (ANOVA), Q value = 1

Table S76. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 89.1 (9.4)
subtype1 | 3 | 80.0 (10.0)
subtype3 | 5 | 92.0 (8.4)
subtype4 | 3 | 93.3 (5.8)

Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.497 (ANOVA), Q value = 1

Table S77. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 27.5 (21.5)
subtype1 | 3 | 31.7 (14.6)
subtype3 | 4 | 34.2 (30.2)
subtype4 | 3 | 14.3 (11.2)

Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Methods & Data
Input

- Cluster data file = KICH-TP.mergedcluster.txt

- Clinical data file = KICH-TP.clin.merged.picked.txt

- Number of patients = 64

- Number of clustering approaches = 8

- Number of selected clinical features = 10

- 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

ANOVA analysis

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

Chi-square test

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

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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