Head and Neck Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 11 clinical features across 315 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

- 7 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death', 'NUMBERPACKYEARSSMOKED', and 'TOBACCOSMOKINGHISTORYINDICATOR'.

- 8 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'TOBACCOSMOKINGHISTORYINDICATOR'.

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

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that 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 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.

Clinical Features | Statistical Tests | CN CNMF | METHLYATION CNMF | RPPA CNMF subtypes | RPPA cHierClus subtypes | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 8.76e-05 (0.00771) | 0.0554 (1.00) | 0.109 (1.00) | 0.265 (1.00) | 0.448 (1.00) | 0.205 (1.00) | 0.94 (1.00) | 0.574 (1.00)
AGE | ANOVA | 0.193 (1.00) | 0.000106 (0.00922) | 0.434 (1.00) | 0.303 (1.00) | 0.925 (1.00) | 0.383 (1.00) | 0.73 (1.00) | 0.688 (1.00)
GENDER | Fisher's exact test | 0.71 (1.00) | 0.0445 (1.00) | 0.139 (1.00) | 0.842 (1.00) | 0.188 (1.00) | 0.00985 (0.808) | 0.0643 (1.00) | 0.637 (1.00)
PATHOLOGY T | Chi-square test | 0.473 (1.00) | 0.0887 (1.00) | 0.216 (1.00) | 0.194 (1.00) | 0.225 (1.00) | 0.298 (1.00) | 0.121 (1.00) | 0.395 (1.00)
PATHOLOGY N | Chi-square test | 0.0188 (1.00) | 0.439 (1.00) | 0.0063 (0.523) | 0.0534 (1.00) | 0.194 (1.00) | 0.0772 (1.00) | 0.109 (1.00) | 0.191 (1.00)
TUMOR STAGE | Chi-square test | 0.486 (1.00) | 0.272 (1.00) | 0.0532 (1.00) | 0.103 (1.00) | 0.0364 (1.00) | 0.39 (1.00) | 0.451 (1.00) | 0.321 (1.00)
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.86 (1.00) | 0.715 (1.00) | 0.301 (1.00) | 0.595 (1.00) | 0.413 (1.00) | 0.546 (1.00) | 0.533 (1.00) | 0.551 (1.00)
NUMBERPACKYEARSSMOKED | ANOVA | 0.00135 (0.114) | 0.0491 (1.00) | 0.658 (1.00) | 0.752 (1.00) | 0.0646 (1.00) | 0.0544 (1.00) | 0.413 (1.00) | 0.315 (1.00)
STOPPEDSMOKINGYEAR | ANOVA | 0.362 (1.00) | 0.19 (1.00) | 0.79 (1.00) | 0.37 (1.00) | 0.587 (1.00) | 0.724 (1.00) | 0.632 (1.00) | 0.447 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR | Chi-square test | 0.000205 (0.0176) | 0.00052 (0.0442) | 0.167 (1.00) | 0.433 (1.00) | 0.42 (1.00) | 0.129 (1.00) | 0.217 (1.00) | 0.624 (1.00)
YEAROFTOBACCOSMOKINGONSET | ANOVA | 0.801 (1.00) | 0.0163 (1.00) | 0.539 (1.00) | 0.746 (1.00) | 0.133 (1.00) | 0.125 (1.00) | 0.366 (1.00) | 0.213 (1.00)

Clustering Approach #1: 'CN CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7
--- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 64 | 41 | 45 | 83 | 20 | 10 | 46

'CN CNMF' versus 'Time to Death'

P value = 8.76e-05 (logrank test), Q value = 0.0077

Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 306 | 121 | 0.1 - 210.9 (14.2)
subtype1 | 64 | 34 | 0.2 - 135.3 (12.4)
subtype2 | 41 | 18 | 1.0 - 126.1 (16.6)
subtype3 | 45 | 16 | 1.0 - 142.5 (16.3)
subtype4 | 83 | 18 | 0.8 - 210.9 (17.8)
subtype5 | 19 | 8 | 0.1 - 84.2 (12.9)
subtype6 | 10 | 3 | 1.5 - 58.4 (25.5)
subtype7 | 44 | 24 | 0.1 - 126.1 (12.2)

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

'CN CNMF' versus 'AGE'

P value = 0.193 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 309 | 61.0 (12.0)
subtype1 | 64 | 60.7 (11.4)
subtype2 | 41 | 56.8 (15.7)
subtype3 | 45 | 62.5 (9.4)
subtype4 | 83 | 62.4 (13.0)
subtype5 | 20 | 61.2 (8.8)
subtype6 | 10 | 56.8 (8.8)
subtype7 | 46 | 62.3 (10.8)

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

'CN CNMF' versus 'GENDER'

P value = 0.71 (Chi-square test), Q value = 1

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 84 | 225
subtype1 | 16 | 48
subtype2 | 9 | 32
subtype3 | 11 | 34
subtype4 | 29 | 54
subtype5 | 5 | 15
subtype6 | 2 | 8
subtype7 | 12 | 34

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.473 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 25 | 77 | 63 | 101
subtype1 | 6 | 11 | 11 | 24
subtype2 | 4 | 10 | 6 | 16
subtype3 | 2 | 10 | 13 | 14
subtype4 | 10 | 25 | 12 | 22
subtype5 | 0 | 7 | 6 | 4
subtype6 | 0 | 3 | 3 | 3
subtype7 | 3 | 11 | 12 | 18

Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.0188 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 102 | 32 | 98 | 5
subtype1 | 14 | 5 | 28 | 2
subtype2 | 14 | 5 | 9 | 1
subtype3 | 15 | 2 | 17 | 1
subtype4 | 37 | 8 | 14 | 0
subtype5 | 5 | 2 | 9 | 0
subtype6 | 1 | 3 | 4 | 1
subtype7 | 16 | 7 | 17 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.486 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 18 | 47 | 42 | 154
subtype1 | 5 | 5 | 5 | 37
subtype2 | 4 | 7 | 6 | 19
subtype3 | 2 | 6 | 6 | 24
subtype4 | 4 | 16 | 15 | 30
subtype5 | 0 | 2 | 4 | 11
subtype6 | 0 | 1 | 2 | 6
subtype7 | 3 | 10 | 4 | 27

Figure S6. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.86 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 76 | 233
subtype1 | 19 | 45
subtype2 | 9 | 32
subtype3 | 12 | 33
subtype4 | 21 | 62
subtype5 | 5 | 15
subtype6 | 2 | 8
subtype7 | 8 | 38

Figure S7. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.00135 (ANOVA), Q value = 0.11

Table S9. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 167 | 49.8 (37.1)
subtype1 | 40 | 64.9 (53.1)
subtype2 | 17 | 49.1 (25.1)
subtype3 | 31 | 63.6 (36.5)
subtype4 | 44 | 35.1 (23.3)
subtype5 | 10 | 37.8 (17.8)
subtype6 | 4 | 34.8 (33.7)
subtype7 | 21 | 40.4 (22.8)

Figure S8. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.362 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 120 | 1994.5 (13.8)
subtype1 | 21 | 1996.0 (13.7)
subtype2 | 10 | 2000.2 (5.6)
subtype3 | 21 | 1996.4 (12.1)
subtype4 | 37 | 1990.0 (15.4)
subtype5 | 10 | 1996.8 (14.9)
subtype6 | 3 | 1997.0 (5.6)
subtype7 | 18 | 1994.6 (14.8)

Figure S9. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000205 (Chi-square test), Q value = 0.018

Table S11. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 81 | 56 | 97 | 64
subtype1 | 15 | 10 | 32 | 5
subtype2 | 13 | 1 | 11 | 14
subtype3 | 15 | 8 | 18 | 2
subtype4 | 18 | 21 | 19 | 24
subtype5 | 7 | 4 | 3 | 6
subtype6 | 3 | 0 | 3 | 4
subtype7 | 10 | 12 | 11 | 9

Figure S10. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'CN CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.801 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 174 | 1964.6 (12.3)
subtype1 | 43 | 1965.7 (11.5)
subtype2 | 18 | 1966.3 (13.5)
subtype3 | 31 | 1963.3 (11.0)
subtype4 | 45 | 1963.1 (14.2)
subtype5 | 8 | 1967.9 (7.4)
subtype6 | 5 | 1969.4 (6.8)
subtype7 | 24 | 1964.0 (13.1)

Figure S11. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
--- | --- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 43 | 47 | 40 | 44 | 58 | 53 | 5 | 19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0554 (logrank test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 306 | 121 | 0.1 - 210.9 (14.6)
subtype1 | 43 | 15 | 0.1 - 142.5 (13.2)
subtype2 | 47 | 19 | 0.2 - 126.1 (13.6)
subtype3 | 39 | 20 | 0.1 - 156.5 (12.7)
subtype4 | 44 | 8 | 0.8 - 95.0 (22.3)
subtype5 | 58 | 30 | 0.5 - 135.3 (14.4)
subtype6 | 51 | 19 | 1.5 - 210.9 (14.3)
subtype7 | 5 | 1 | 5.0 - 28.6 (10.7)
subtype8 | 19 | 9 | 1.0 - 84.5 (18.0)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000106 (ANOVA), Q value = 0.0092

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 309 | 61.1 (12.1)
subtype1 | 43 | 58.0 (10.6)
subtype2 | 47 | 55.5 (13.8)
subtype3 | 40 | 62.4 (11.1)
subtype4 | 44 | 58.1 (10.9)
subtype5 | 58 | 64.4 (10.1)
subtype6 | 53 | 63.9 (13.2)
subtype7 | 5 | 63.8 (11.7)
subtype8 | 19 | 67.4 (10.3)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0445 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 85 | 224
subtype1 | 7 | 36
subtype2 | 17 | 30
subtype3 | 15 | 25
subtype4 | 5 | 39
subtype5 | 15 | 43
subtype6 | 18 | 35
subtype7 | 1 | 4
subtype8 | 7 | 12

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0887 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 78 | 63 | 102
subtype1 | 2 | 10 | 11 | 17
subtype2 | 7 | 14 | 12 | 10
subtype3 | 1 | 10 | 5 | 19
subtype4 | 4 | 9 | 5 | 8
subtype5 | 1 | 13 | 13 | 23
subtype6 | 8 | 18 | 8 | 17
subtype7 | 1 | 0 | 2 | 1
subtype8 | 0 | 4 | 7 | 7

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.439 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 101 | 32 | 100 | 5
subtype1 | 21 | 4 | 11 | 1
subtype2 | 19 | 4 | 14 | 0
subtype3 | 15 | 4 | 9 | 2
subtype4 | 9 | 5 | 9 | 0
subtype5 | 12 | 5 | 24 | 2
subtype6 | 18 | 8 | 22 | 0
subtype7 | 1 | 0 | 3 | 0
subtype8 | 6 | 2 | 8 | 0

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.272 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 17 | 47 | 42 | 156
subtype1 | 1 | 7 | 8 | 24
subtype2 | 6 | 9 | 10 | 17
subtype3 | 1 | 8 | 2 | 24
subtype4 | 2 | 3 | 6 | 13
subtype5 | 1 | 8 | 6 | 34
subtype6 | 5 | 9 | 7 | 29
subtype7 | 1 | 0 | 0 | 3
subtype8 | 0 | 3 | 3 | 12

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.715 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 78 | 231
subtype1 | 7 | 36
subtype2 | 10 | 37
subtype3 | 11 | 29
subtype4 | 10 | 34
subtype5 | 16 | 42
subtype6 | 17 | 36
subtype7 | 2 | 3
subtype8 | 5 | 14

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0491 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 168 | 49.5 (37.2)
subtype1 | 29 | 56.3 (30.5)
subtype2 | 15 | 33.5 (20.9)
subtype3 | 18 | 55.2 (64.5)
subtype4 | 22 | 41.8 (44.6)
subtype5 | 36 | 64.1 (33.8)
subtype6 | 31 | 42.9 (25.7)
subtype7 | 4 | 47.2 (23.6)
subtype8 | 13 | 34.2 (21.4)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.19 (ANOVA), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 121 | 1994.5 (13.8)
subtype1 | 13 | 1999.8 (12.8)
subtype2 | 12 | 1995.8 (11.7)
subtype3 | 14 | 1995.7 (15.2)
subtype4 | 19 | 1994.3 (12.2)
subtype5 | 28 | 1996.3 (12.1)
subtype6 | 22 | 1988.0 (15.9)
subtype7 | 3 | 2005.3 (8.1)
subtype8 | 10 | 1990.7 (16.7)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00052 (Chi-square test), Q value = 0.044

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 82 | 55 | 99 | 62
subtype1 | 12 | 7 | 21 | 2
subtype2 | 7 | 10 | 9 | 18
subtype3 | 9 | 6 | 11 | 11
subtype4 | 14 | 6 | 11 | 13
subtype5 | 23 | 8 | 21 | 3
subtype6 | 8 | 14 | 17 | 13
subtype7 | 3 | 0 | 2 | 0
subtype8 | 6 | 4 | 7 | 2

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0163 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 175 | 1964.6 (12.1)
subtype1 | 25 | 1965.2 (9.2)
subtype2 | 18 | 1971.8 (12.9)
subtype3 | 19 | 1967.8 (11.9)
subtype4 | 26 | 1965.3 (11.8)
subtype5 | 36 | 1962.4 (11.1)
subtype6 | 34 | 1964.2 (12.4)
subtype7 | 5 | 1963.4 (11.3)
subtype8 | 12 | 1954.9 (14.8)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 82 | 85 | 45

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.109 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 212 | 107 | 0.1 - 210.9 (13.2)
subtype1 | 82 | 42 | 1.5 - 129.2 (12.1)
subtype2 | 85 | 47 | 0.1 - 210.9 (13.1)
subtype3 | 45 | 18 | 2.1 - 156.5 (20.5)

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 62.1 (12.2)
subtype1 | 82 | 61.1 (12.1)
subtype2 | 85 | 63.4 (12.1)
subtype3 | 45 | 61.4 (12.7)

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.139 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 150
subtype1 | 19 | 63
subtype2 | 25 | 60
subtype3 | 18 | 27

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 13 | 59 | 53 | 79
subtype1 | 3 | 24 | 16 | 34
subtype2 | 6 | 18 | 27 | 32
subtype3 | 4 | 17 | 10 | 13

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0063 (Chi-square test), Q value = 0.52

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 73 | 20 | 79 | 4
subtype1 | 27 | 9 | 30 | 1
subtype2 | 19 | 8 | 40 | 2
subtype3 | 27 | 3 | 9 | 1

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0532 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 9 | 39 | 31 | 121
subtype1 | 1 | 17 | 12 | 47
subtype2 | 4 | 9 | 12 | 55
subtype3 | 4 | 13 | 7 | 19

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.301 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 56 | 156
subtype1 | 25 | 57
subtype2 | 23 | 62
subtype3 | 8 | 37

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.658 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 105 | 48.9 (38.6)
subtype1 | 39 | 48.3 (34.1)
subtype2 | 44 | 46.2 (29.7)
subtype3 | 22 | 55.4 (58.5)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.79 (ANOVA), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 85 | 1994.1 (14.2)
subtype1 | 25 | 1995.7 (13.1)
subtype2 | 41 | 1993.2 (13.8)
subtype3 | 19 | 1993.8 (16.8)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.167 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 60 | 38 | 64 | 40
subtype1 | 20 | 10 | 28 | 19
subtype2 | 27 | 21 | 24 | 10
subtype3 | 13 | 7 | 12 | 11

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.539 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 115 | 1964.2 (12.4)
subtype1 | 43 | 1965.9 (14.1)
subtype2 | 49 | 1963.0 (11.7)
subtype3 | 23 | 1963.7 (10.9)

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 115 | 81 | 16

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.265 (logrank test), Q value = 1

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 212 | 107 | 0.1 - 210.9 (13.2)
subtype1 | 115 | 56 | 0.1 - 210.9 (13.2)
subtype2 | 81 | 42 | 1.5 - 156.5 (13.6)
subtype3 | 16 | 9 | 3.3 - 52.3 (11.6)

Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.303 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 62.1 (12.2)
subtype1 | 115 | 63.3 (11.2)
subtype2 | 81 | 61.1 (13.1)
subtype3 | 16 | 59.4 (14.3)

Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.842 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 150
subtype1 | 35 | 80
subtype2 | 22 | 59
subtype3 | 5 | 11

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

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

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 13 | 59 | 53 | 79
subtype1 | 7 | 25 | 36 | 42
subtype2 | 5 | 30 | 13 | 30
subtype3 | 1 | 4 | 4 | 7

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0534 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 73 | 20 | 79 | 4
subtype1 | 39 | 8 | 46 | 3
subtype2 | 32 | 11 | 22 | 1
subtype3 | 2 | 1 | 11 | 0

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.103 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 9 | 39 | 31 | 121
subtype1 | 6 | 16 | 18 | 69
subtype2 | 3 | 21 | 13 | 39
subtype3 | 0 | 2 | 0 | 13

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.595 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 56 | 156
subtype1 | 30 | 85
subtype2 | 20 | 61
subtype3 | 6 | 10

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.752 (ANOVA), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 105 | 48.9 (38.6)
subtype1 | 60 | 47.2 (31.5)
subtype2 | 37 | 49.6 (50.0)
subtype3 | 8 | 58.1 (28.6)

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.37 (ANOVA), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 85 | 1994.1 (14.2)
subtype1 | 53 | 1994.4 (13.7)
subtype2 | 27 | 1992.0 (16.0)
subtype3 | 5 | 2001.6 (5.9)

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.433 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 60 | 38 | 64 | 40
subtype1 | 34 | 26 | 33 | 18
subtype2 | 22 | 11 | 24 | 18
subtype3 | 4 | 1 | 7 | 4

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.746 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 115 | 1964.2 (12.4)
subtype1 | 66 | 1963.7 (10.8)
subtype2 | 41 | 1964.5 (15.4)
subtype3 | 8 | 1967.2 (7.9)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 118 | 93 | 91

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.448 (logrank test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 299 | 120 | 0.1 - 210.9 (14.8)
subtype1 | 118 | 42 | 0.1 - 135.3 (14.2)
subtype2 | 91 | 40 | 0.2 - 142.5 (15.7)
subtype3 | 90 | 38 | 0.1 - 210.9 (14.0)

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.925 (ANOVA), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 302 | 61.0 (12.2)
subtype1 | 118 | 61.3 (11.1)
subtype2 | 93 | 60.6 (12.8)
subtype3 | 91 | 61.2 (12.9)

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.188 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 81 | 221
subtype1 | 25 | 93
subtype2 | 27 | 66
subtype3 | 29 | 62

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.225 (Chi-square test), Q value = 1

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 23 | 78 | 60 | 101
subtype1 | 6 | 25 | 24 | 36
subtype2 | 13 | 25 | 19 | 29
subtype3 | 4 | 28 | 17 | 36

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

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

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 100 | 32 | 97 | 5
subtype1 | 34 | 10 | 32 | 3
subtype2 | 28 | 10 | 42 | 1
subtype3 | 38 | 12 | 23 | 1

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0364 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 47 | 41 | 153
subtype1 | 2 | 15 | 17 | 54
subtype2 | 11 | 12 | 10 | 52
subtype3 | 3 | 20 | 14 | 47

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.413 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 227
subtype1 | 25 | 93
subtype2 | 27 | 66
subtype3 | 23 | 68

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0646 (ANOVA), Q value = 1

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 165 | 50.0 (37.4)
subtype1 | 75 | 56.7 (36.5)
subtype2 | 41 | 48.5 (50.4)
subtype3 | 49 | 40.9 (21.1)

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.587 (ANOVA), Q value = 1

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 119 | 1994.6 (13.9)
subtype1 | 49 | 1995.7 (13.1)
subtype2 | 34 | 1995.0 (13.4)
subtype3 | 36 | 1992.6 (15.6)

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.42 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 82 | 53 | 98 | 58
subtype1 | 37 | 17 | 42 | 17
subtype2 | 24 | 16 | 29 | 21
subtype3 | 21 | 20 | 27 | 20

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.133 (ANOVA), Q value = 1

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 173 | 1964.5 (12.1)
subtype1 | 73 | 1963.0 (11.0)
subtype2 | 49 | 1967.4 (11.5)
subtype3 | 51 | 1964.1 (13.8)

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 97 | 85 | 120

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.205 (logrank test), Q value = 1

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 299 | 120 | 0.1 - 210.9 (14.8)
subtype1 | 95 | 43 | 1.5 - 142.5 (15.1)
subtype2 | 84 | 36 | 0.1 - 210.9 (14.0)
subtype3 | 120 | 41 | 0.1 - 135.3 (15.1)

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.383 (ANOVA), Q value = 1

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 302 | 61.0 (12.2)
subtype1 | 97 | 60.0 (13.3)
subtype2 | 85 | 62.5 (12.9)
subtype3 | 120 | 60.9 (10.5)

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00985 (Fisher's exact test), Q value = 0.81

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 81 | 221
subtype1 | 31 | 66
subtype2 | 29 | 56
subtype3 | 21 | 99

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.298 (Chi-square test), Q value = 1

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 23 | 78 | 60 | 101
subtype1 | 13 | 27 | 22 | 30
subtype2 | 4 | 26 | 15 | 32
subtype3 | 6 | 25 | 23 | 39

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0772 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 100 | 32 | 97 | 5
subtype1 | 27 | 12 | 45 | 1
subtype2 | 37 | 10 | 20 | 1
subtype3 | 36 | 10 | 32 | 3

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.39 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 47 | 41 | 153
subtype1 | 9 | 14 | 12 | 56
subtype2 | 4 | 18 | 13 | 41
subtype3 | 3 | 15 | 16 | 56

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.546 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 227
subtype1 | 28 | 69
subtype2 | 20 | 65
subtype3 | 27 | 93

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0544 (ANOVA), Q value = 1

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 165 | 50.0 (37.4)
subtype1 | 44 | 46.6 (49.1)
subtype2 | 42 | 40.5 (20.5)
subtype3 | 79 | 56.9 (35.8)

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.724 (ANOVA), Q value = 1

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 119 | 1994.6 (13.9)
subtype1 | 37 | 1993.6 (14.0)
subtype2 | 34 | 1993.9 (15.5)
subtype3 | 48 | 1995.8 (12.9)

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.129 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 82 | 53 | 98 | 58
subtype1 | 24 | 19 | 29 | 23
subtype2 | 21 | 17 | 22 | 20
subtype3 | 37 | 17 | 47 | 15

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.125 (ANOVA), Q value = 1

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 173 | 1964.5 (12.1)
subtype1 | 51 | 1967.4 (11.8)
subtype2 | 44 | 1963.9 (14.2)
subtype3 | 78 | 1963.0 (10.8)

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S73. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 83 | 131 | 94

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.94 (logrank test), Q value = 1

Table S74. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 305 | 122 | 0.1 - 210.9 (15.0)
subtype1 | 82 | 30 | 0.1 - 210.9 (12.1)
subtype2 | 131 | 52 | 0.1 - 142.5 (14.3)
subtype3 | 92 | 40 | 1.8 - 126.1 (18.1)

Figure S67. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.73 (ANOVA), Q value = 1

Table S75. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 308 | 61.1 (12.1)
subtype1 | 83 | 60.4 (12.5)
subtype2 | 131 | 61.7 (11.4)
subtype3 | 94 | 60.9 (12.7)

Figure S68. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0643 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 222
subtype1 | 16 | 67
subtype2 | 37 | 94
subtype3 | 33 | 61

Figure S69. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.121 (Chi-square test), Q value = 1

Table S77. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 78 | 62 | 103
subtype1 | 5 | 23 | 11 | 30
subtype2 | 7 | 26 | 29 | 47
subtype3 | 12 | 29 | 22 | 26

Figure S70. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.109 (Chi-square test), Q value = 1

Table S78. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 99 | 32 | 101 | 5
subtype1 | 30 | 8 | 21 | 2
subtype2 | 44 | 9 | 41 | 3
subtype3 | 25 | 15 | 39 | 0

Figure S71. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.451 (Chi-square test), Q value = 1

Table S79. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 17 | 46 | 41 | 158
subtype1 | 4 | 15 | 8 | 41
subtype2 | 4 | 16 | 18 | 68
subtype3 | 9 | 15 | 15 | 49

Figure S72. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.533 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 77 | 231
subtype1 | 21 | 62
subtype2 | 29 | 102
subtype3 | 27 | 67

Figure S73. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.413 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 169 | 49.8 (37.1)
subtype1 | 53 | 45.8 (25.8)
subtype2 | 76 | 54.0 (35.3)
subtype3 | 40 | 47.1 (51.0)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.632 (ANOVA), Q value = 1

Table S82. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 121 | 1994.4 (13.8)
subtype1 | 31 | 1993.5 (13.6)
subtype2 | 54 | 1995.7 (13.8)
subtype3 | 36 | 1993.1 (14.1)

Figure S75. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

Table S83. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 82 | 55 | 99 | 61
subtype1 | 21 | 16 | 29 | 15
subtype2 | 39 | 18 | 46 | 21
subtype3 | 22 | 21 | 24 | 25

Figure S76. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.366 (ANOVA), Q value = 1

Table S84. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 176 | 1964.5 (12.1)
subtype1 | 52 | 1964.9 (13.4)
subtype2 | 76 | 1963.2 (11.6)
subtype3 | 48 | 1966.3 (11.3)

Figure S77. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S85. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 13 | 12 | 167 | 116

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.574 (logrank test), Q value = 1

Table S86. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 305 | 122 | 0.1 - 210.9 (15.0)
subtype1 | 13 | 5 | 0.2 - 56.7 (12.0)
subtype2 | 12 | 5 | 9.0 - 84.4 (12.8)
subtype3 | 166 | 61 | 0.1 - 210.9 (14.0)
subtype4 | 114 | 51 | 0.5 - 156.5 (17.1)

Figure S78. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.688 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 308 | 61.1 (12.1)
subtype1 | 13 | 58.4 (13.2)
subtype2 | 12 | 60.8 (12.0)
subtype3 | 167 | 61.8 (11.5)
subtype4 | 116 | 60.4 (12.9)

Figure S79. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 222
subtype1 | 4 | 9
subtype2 | 4 | 8
subtype3 | 42 | 125
subtype4 | 36 | 80

Figure S80. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.395 (Chi-square test), Q value = 1

Table S89. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 78 | 62 | 103
subtype1 | 2 | 5 | 2 | 3
subtype2 | 0 | 3 | 2 | 5
subtype3 | 8 | 38 | 30 | 60
subtype4 | 14 | 32 | 28 | 35

Figure S81. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.191 (Chi-square test), Q value = 1

Table S90. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 99 | 32 | 101 | 5
subtype1 | 8 | 1 | 3 | 0
subtype2 | 5 | 0 | 5 | 0
subtype3 | 54 | 17 | 42 | 4
subtype4 | 32 | 14 | 51 | 1

Figure S82. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.321 (Chi-square test), Q value = 1

Table S91. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 17 | 46 | 41 | 158
subtype1 | 2 | 4 | 2 | 4
subtype2 | 0 | 1 | 1 | 8
subtype3 | 5 | 24 | 23 | 80
subtype4 | 10 | 17 | 15 | 66

Figure S83. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.551 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 77 | 231
subtype1 | 3 | 10
subtype2 | 4 | 8
subtype3 | 37 | 130
subtype4 | 33 | 83

Figure S84. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.315 (ANOVA), Q value = 1

Table S93. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 169 | 49.8 (37.1)
subtype1 | 7 | 60.2 (25.8)
subtype2 | 5 | 26.9 (16.2)
subtype3 | 102 | 52.3 (33.3)
subtype4 | 55 | 45.8 (45.0)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.447 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 121 | 1994.4 (13.8)
subtype1 | 3 | 1998.3 (9.5)
subtype2 | 4 | 1994.8 (13.2)
subtype3 | 68 | 1995.9 (13.3)
subtype4 | 46 | 1991.8 (14.6)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.624 (Chi-square test), Q value = 1

Table S95. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients | CURRENT REFORMED SMOKER FOR < OR = 15 YEARS | CURRENT REFORMED SMOKER FOR > 15 YEARS | CURRENT SMOKER | LIFELONG NON-SMOKER
--- | --- | --- | --- | ---
ALL | 82 | 55 | 99 | 61
subtype1 | 3 | 2 | 4 | 4
subtype2 | 3 | 1 | 4 | 3
subtype3 | 50 | 25 | 56 | 28
subtype4 | 26 | 27 | 35 | 26

Figure S87. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.213 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 176 | 1964.5 (12.1)
subtype1 | 5 | 1964.8 (7.7)
subtype2 | 6 | 1973.7 (9.1)
subtype3 | 100 | 1963.5 (11.2)
subtype4 | 65 | 1965.3 (13.6)

Figure S88. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data
Input

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

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

- Number of patients = 315

- Number of clustering approaches = 8

- Number of selected clinical features = 11

- 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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- 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] 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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