Lung Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 10 different clustering approaches and 11 clinical features across 292 patients, 15 significant findings detected with P value < 0.05.

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

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

- 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE'.

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

- CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.

- Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'AGE', 'PATHOLOGY.N', and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', and 'PATHOLOGY.N'.

- 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 3 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 10 different clustering approaches and 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 15 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | 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 | 0.337 | 0.671 | 0.694 | 0.0038 | 0.0207 | 0.0117 | 8.14e-06 | 0.00236 | 0.0944 | 0.118
AGE | ANOVA | 0.477 | 0.557 | 1.52e-05 | 0.0274 | 0.0204 | 0.00368 | 0.00378 | 0.0518 | 0.329 | 0.0539
GENDER | Fisher's exact test | 0.272 | 0.383 | 0.675 | 0.189 | 0.211 | 0.222 | 0.0537 | 0.00756 | 0.83 | 0.0731
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  | 0.365 | 0.602 | 0.0806 | 0.295 | 0.411 | 0.603 | 0.919 | 0.59
HISTOLOGICAL TYPE | Chi-square test | 0.3 | 0.274 | 0.125 | 0.102 | 0.187 | 0.00941 | 0.271 | 0.301 | 0.152 | 0.442
PATHOLOGY T | Chi-square test | 0.489 | 0.479 | 0.605 | 0.616 | 0.165 | 0.67 | 0.182 | 0.369 | 0.625 | 0.614
PATHOLOGY N | Chi-square test | 0.572 | 0.651 | 0.803 | 0.324 | 0.54 | 0.51 | 0.00208 | 0.0485 | 0.753 | 0.223
PATHOLOGICSPREAD(M) | Chi-square test | 0.504 | 1 | 0.853 | 0.591 | 0.913 | 0.683 | 0.792 | 0.901 | 0.292 | 0.4
TUMOR STAGE | Chi-square test | 0.147 | 0.274 | 0.125 | 0.576 | 0.908 | 0.982 | 0.072 | 0.111 | 0.625 | 0.174
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1 | 0.302 | 0.0949 | 1 | 0.415 | 0.652 | 0.508 | 0.905 | 0.784
NEOADJUVANT THERAPY | Fisher's exact test | 0.921 | 1 | 0.461 | 0.859 | 0.175 | 0.633 | 0.0262 | 0.0686 | 0.289 | 0.0979

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 5 | 9 | 12 | 6

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.337 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 31 | 4 | 0.5 - 56.8 (8.3)
subtype1 | 4 | 0 | 6.0 - 48.6 (9.7)
subtype2 | 9 | 1 | 4.0 - 56.8 (8.3)
subtype3 | 12 | 1 | 0.5 - 37.0 (3.3)
subtype4 | 6 | 2 | 2.0 - 45.2 (30.9)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.477 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 30 | 65.7 (10.8)
subtype1 | 4 | 58.5 (15.5)
subtype2 | 9 | 65.0 (9.1)
subtype3 | 12 | 67.1 (11.1)
subtype4 | 5 | 69.4 (9.0)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.272 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 18 | 14
subtype1 | 3 | 2
subtype2 | 3 | 6
subtype3 | 9 | 3
subtype4 | 3 | 3

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.3 (Chi-square test)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA
--- | --- | --- | ---
ALL | 1 | 30 | 1
subtype1 | 0 | 4 | 1
subtype2 | 0 | 9 | 0
subtype3 | 1 | 11 | 0
subtype4 | 0 | 6 | 0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.489 (Chi-square test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3
--- | --- | --- | ---
ALL | 12 | 19 | 1
subtype1 | 3 | 2 | 0
subtype2 | 4 | 4 | 1
subtype3 | 4 | 8 | 0
subtype4 | 1 | 5 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.572 (Chi-square test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 23 | 4 | 4
subtype1 | 3 | 1 | 1
subtype2 | 8 | 1 | 0
subtype3 | 9 | 1 | 1
subtype4 | 3 | 1 | 2

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.504 (Fisher's exact test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 30 | 2
subtype1 | 4 | 1
subtype2 | 9 | 0
subtype3 | 11 | 1
subtype4 | 6 | 0

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.147 (Chi-square test)

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 23 | 4 | 3 | 2
subtype1 | 3 | 1 | 0 | 1
subtype2 | 8 | 0 | 1 | 0
subtype3 | 10 | 1 | 0 | 1
subtype4 | 2 | 2 | 2 | 0

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 1 | 31
subtype1 | 0 | 5
subtype2 | 0 | 9
subtype3 | 1 | 11
subtype4 | 0 | 6

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.921 (Fisher's exact test)

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 6 | 26
subtype1 | 1 | 4
subtype2 | 1 | 8
subtype3 | 3 | 9
subtype4 | 1 | 5

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 7 | 13 | 12

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.671 (logrank test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 31 | 4 | 0.5 - 56.8 (8.3)
subtype1 | 7 | 2 | 2.0 - 48.6 (38.7)
subtype2 | 13 | 1 | 0.5 - 37.0 (3.4)
subtype3 | 11 | 1 | 4.0 - 56.8 (8.1)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.557 (ANOVA)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 30 | 65.7 (10.8)
subtype1 | 5 | 69.4 (9.0)
subtype2 | 13 | 66.5 (10.9)
subtype3 | 12 | 63.3 (11.6)

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.383 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 18 | 14
subtype1 | 4 | 3
subtype2 | 9 | 4
subtype3 | 5 | 7

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Chi-square test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA
--- | --- | --- | ---
ALL | 1 | 30 | 1
subtype1 | 0 | 6 | 1
subtype2 | 1 | 12 | 0
subtype3 | 0 | 12 | 0

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.479 (Chi-square test)

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3
--- | --- | --- | ---
ALL | 12 | 19 | 1
subtype1 | 2 | 5 | 0
subtype2 | 4 | 9 | 0
subtype3 | 6 | 5 | 1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.651 (Chi-square test)

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 23 | 4 | 4
subtype1 | 4 | 1 | 2
subtype2 | 10 | 1 | 1
subtype3 | 9 | 2 | 1

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 30 | 2
subtype1 | 7 | 0
subtype2 | 12 | 1
subtype3 | 11 | 1

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.274 (Chi-square test)

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 23 | 4 | 3 | 2
subtype1 | 3 | 2 | 2 | 0
subtype2 | 11 | 1 | 0 | 1
subtype3 | 9 | 1 | 1 | 1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 1 | 31
subtype1 | 0 | 7
subtype2 | 1 | 12
subtype3 | 0 | 12

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 6 | 26
subtype1 | 1 | 6
subtype2 | 3 | 10
subtype3 | 2 | 10

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S23. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 85 | 126 | 77

'CN CNMF' versus 'Time to Death'

P value = 0.694 (logrank test)

Table S24. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 254 | 81 | 0.0 - 224.0 (13.4)
subtype1 | 75 | 26 | 0.1 - 224.0 (12.1)
subtype2 | 113 | 37 | 0.0 - 104.2 (13.2)
subtype3 | 66 | 18 | 0.0 - 88.1 (14.9)

Figure S21. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 1.52e-05 (ANOVA)

Table S25. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 257 | 65.4 (9.7)
subtype1 | 78 | 63.1 (10.4)
subtype2 | 111 | 68.6 (8.0)
subtype3 | 68 | 62.9 (9.9)

Figure S22. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.675 (Fisher's exact test)

Table S26. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 156 | 132
subtype1 | 49 | 36
subtype2 | 68 | 58
subtype3 | 39 | 38

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

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.365 (ANOVA)

Table S27. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 74.2 (33.7)
subtype1 | 8 | 60.0 (38.5)
subtype2 | 10 | 82.0 (29.7)
subtype3 | 8 | 78.8 (33.1)

Figure S24. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.125 (Chi-square test)

Table S28. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 65 | 182 | 3 | 10 | 2 | 3 | 2 | 10 | 1 | 4
subtype1 | 0 | 16 | 55 | 1 | 7 | 1 | 1 | 0 | 3 | 1 | 0
subtype2 | 3 | 32 | 74 | 2 | 2 | 0 | 1 | 2 | 6 | 0 | 4
subtype3 | 3 | 17 | 53 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0

Figure S25. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.605 (Chi-square test)

Table S29. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 80 | 169 | 22 | 16
subtype1 | 24 | 53 | 5 | 3
subtype2 | 34 | 76 | 9 | 6
subtype3 | 22 | 40 | 8 | 7

Figure S26. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.803 (Chi-square test)

Table S30. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 172 | 58 | 52
subtype1 | 50 | 15 | 17
subtype2 | 79 | 25 | 20
subtype3 | 43 | 18 | 15

Figure S27. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.853 (Chi-square test)

Table S31. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 202 | 16 | 63
subtype1 | 57 | 6 | 20
subtype2 | 90 | 5 | 27
subtype3 | 55 | 5 | 16

Figure S28. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.125 (Chi-square test)

Table S32. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 149 | 61 | 57 | 16
subtype1 | 44 | 15 | 18 | 7
subtype2 | 74 | 26 | 19 | 4
subtype3 | 31 | 20 | 20 | 5

Figure S29. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.302 (Fisher's exact test)

Table S33. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 19 | 269
subtype1 | 5 | 80
subtype2 | 6 | 120
subtype3 | 8 | 69

Figure S30. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.461 (Fisher's exact test)

Table S34. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 44 | 244
subtype1 | 16 | 69
subtype2 | 16 | 110
subtype3 | 12 | 65

Figure S31. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S35. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 54 | 81 | 87

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0038 (logrank test)

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 194 | 60 | 0.0 - 224.0 (11.3)
subtype1 | 46 | 11 | 0.1 - 224.0 (12.9)
subtype2 | 71 | 18 | 0.0 - 88.1 (10.8)
subtype3 | 77 | 31 | 0.0 - 77.9 (10.6)

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0274 (ANOVA)

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 194 | 65.3 (9.8)
subtype1 | 45 | 68.5 (6.9)
subtype2 | 75 | 63.6 (9.9)
subtype3 | 74 | 65.2 (10.8)

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.189 (Fisher's exact test)

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 120 | 102
subtype1 | 34 | 20
subtype2 | 38 | 43
subtype3 | 48 | 39

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.602 (ANOVA)

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 72.1 (33.6)
subtype1 | 8 | 70.0 (43.4)
subtype2 | 8 | 80.0 (12.0)
subtype3 | 3 | 56.7 (49.3)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.102 (Chi-square test)

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 50 | 134 | 3 | 10 | 1 | 2 | 2 | 9 | 1 | 4
subtype1 | 4 | 10 | 27 | 1 | 5 | 0 | 1 | 2 | 3 | 0 | 1
subtype2 | 1 | 24 | 48 | 1 | 3 | 0 | 1 | 0 | 3 | 0 | 0
subtype3 | 1 | 16 | 59 | 1 | 2 | 1 | 0 | 0 | 3 | 1 | 3

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.616 (Chi-square test)

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 63 | 127 | 17 | 13
subtype1 | 19 | 27 | 5 | 2
subtype2 | 21 | 46 | 7 | 7
subtype3 | 23 | 54 | 5 | 4

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.324 (Chi-square test)

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 131 | 44 | 42
subtype1 | 36 | 9 | 8
subtype2 | 51 | 15 | 13
subtype3 | 44 | 20 | 21

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.591 (Chi-square test)

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 141 | 10 | 65
subtype1 | 34 | 1 | 16
subtype2 | 50 | 6 | 22
subtype3 | 57 | 3 | 27

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.576 (Chi-square test)

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 112 | 47 | 47 | 11
subtype1 | 32 | 9 | 10 | 1
subtype2 | 40 | 17 | 16 | 6
subtype3 | 40 | 21 | 21 | 4

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.0949 (Fisher's exact test)

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 15 | 207
subtype1 | 2 | 52
subtype2 | 3 | 78
subtype3 | 10 | 77

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.859 (Fisher's exact test)

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 32 | 190
subtype1 | 9 | 45
subtype2 | 11 | 70
subtype3 | 12 | 75

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S47. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 89 | 70 | 70

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0207 (logrank test)

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 207 | 66 | 0.0 - 224.0 (12.6)
subtype1 | 74 | 27 | 0.0 - 163.1 (12.2)
subtype2 | 67 | 17 | 0.2 - 224.0 (13.7)
subtype3 | 66 | 22 | 0.0 - 83.8 (9.8)

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0204 (ANOVA)

Table S49. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 208 | 64.8 (9.8)
subtype1 | 77 | 64.5 (10.0)
subtype2 | 67 | 67.3 (9.1)
subtype3 | 64 | 62.6 (9.9)

Figure S44. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.211 (Fisher's exact test)

Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 126 | 103
subtype1 | 55 | 34
subtype2 | 37 | 33
subtype3 | 34 | 36

Figure S45. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0806 (ANOVA)

Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 66.3 (36.2)
subtype1 | 7 | 44.3 (42.8)
subtype2 | 5 | 90.0 (7.1)
subtype3 | 7 | 71.4 (31.8)

Figure S46. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.187 (Chi-square test)

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 49 | 149 | 3 | 7 | 1 | 2 | 2 | 6 | 1 | 3
subtype1 | 2 | 18 | 58 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1
subtype2 | 3 | 19 | 35 | 2 | 4 | 0 | 1 | 0 | 4 | 0 | 2
subtype3 | 1 | 12 | 56 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0

Figure S47. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.165 (Chi-square test)

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 60 | 138 | 17 | 13
subtype1 | 26 | 49 | 9 | 4
subtype2 | 20 | 41 | 2 | 7
subtype3 | 14 | 48 | 6 | 2

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.54 (Chi-square test)

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 133 | 46 | 44
subtype1 | 47 | 22 | 16
subtype2 | 44 | 13 | 12
subtype3 | 42 | 11 | 16

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.913 (Chi-square test)

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 160 | 12 | 53
subtype1 | 62 | 5 | 19
subtype2 | 47 | 4 | 19
subtype3 | 51 | 3 | 15

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.908 (Chi-square test)

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 116 | 48 | 49 | 13
subtype1 | 44 | 20 | 17 | 5
subtype2 | 35 | 16 | 14 | 5
subtype3 | 37 | 12 | 18 | 3

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 15 | 214
subtype1 | 6 | 83
subtype2 | 4 | 66
subtype3 | 5 | 65

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.175 (Fisher's exact test)

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 37 | 192
subtype1 | 15 | 74
subtype2 | 15 | 55
subtype3 | 7 | 63

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S59. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 22 | 72 | 82 | 53

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0117 (logrank test)

Table S60. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 207 | 66 | 0.0 - 224.0 (12.6)
subtype1 | 18 | 4 | 0.0 - 63.7 (7.1)
subtype2 | 68 | 22 | 0.0 - 83.8 (8.9)
subtype3 | 70 | 27 | 0.7 - 163.1 (13.5)
subtype4 | 51 | 13 | 0.1 - 224.0 (15.0)

Figure S54. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00368 (ANOVA)

Table S61. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 208 | 64.8 (9.8)
subtype1 | 19 | 67.9 (7.0)
subtype2 | 66 | 62.6 (11.1)
subtype3 | 73 | 63.5 (9.4)
subtype4 | 50 | 68.4 (8.3)

Figure S55. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test)

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 126 | 103
subtype1 | 14 | 8
subtype2 | 36 | 36
subtype3 | 51 | 31
subtype4 | 25 | 28

Figure S56. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.295 (ANOVA)

Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 66.3 (36.2)
subtype1 | 3 | 60.0 (52.0)
subtype2 | 7 | 71.4 (31.8)
subtype3 | 5 | 44.0 (41.6)
subtype4 | 4 | 90.0 (8.2)

Figure S57. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00941 (Chi-square test)

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 49 | 149 | 3 | 7 | 1 | 2 | 2 | 6 | 1 | 3
subtype1 | 1 | 4 | 12 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0
subtype2 | 2 | 12 | 54 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0
subtype3 | 1 | 17 | 58 | 0 | 2 | 0 | 0 | 0 | 2 | 1 | 1
subtype4 | 2 | 16 | 25 | 3 | 2 | 0 | 0 | 1 | 2 | 0 | 2

Figure S58. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.67 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 60 | 138 | 17 | 13
subtype1 | 7 | 10 | 2 | 3
subtype2 | 18 | 44 | 7 | 3
subtype3 | 21 | 53 | 3 | 4
subtype4 | 14 | 31 | 5 | 3

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.51 (Chi-square test)

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 133 | 46 | 44
subtype1 | 14 | 4 | 4
subtype2 | 44 | 10 | 17
subtype3 | 41 | 21 | 15
subtype4 | 34 | 11 | 8

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.683 (Chi-square test)

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 160 | 12 | 53
subtype1 | 13 | 1 | 8
subtype2 | 52 | 2 | 17
subtype3 | 57 | 6 | 17
subtype4 | 38 | 3 | 11

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.982 (Chi-square test)

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 116 | 48 | 49 | 13
subtype1 | 11 | 5 | 5 | 1
subtype2 | 36 | 14 | 19 | 3
subtype3 | 42 | 18 | 14 | 6
subtype4 | 27 | 11 | 11 | 3

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.415 (Fisher's exact test)

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 15 | 214
subtype1 | 0 | 22
subtype2 | 7 | 65
subtype3 | 6 | 76
subtype4 | 2 | 51

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.633 (Fisher's exact test)

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 37 | 192
subtype1 | 5 | 17
subtype2 | 9 | 63
subtype3 | 14 | 68
subtype4 | 9 | 44

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S71. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 83 | 60 | 45 | 66 | 32

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 8.14e-06 (logrank test)

Table S72. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 252 | 79 | 0.0 - 224.0 (13.4)
subtype1 | 74 | 23 | 0.0 - 163.1 (9.0)
subtype2 | 53 | 12 | 0.1 - 104.2 (13.0)
subtype3 | 40 | 20 | 0.0 - 47.6 (11.3)
subtype4 | 57 | 17 | 0.1 - 88.1 (18.9)
subtype5 | 28 | 7 | 0.1 - 224.0 (14.4)

Figure S65. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00378 (ANOVA)

Table S73. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 255 | 65.6 (9.6)
subtype1 | 74 | 63.9 (10.6)
subtype2 | 54 | 68.6 (7.7)
subtype3 | 40 | 68.7 (9.6)
subtype4 | 59 | 63.3 (9.3)
subtype5 | 28 | 64.6 (8.9)

Figure S66. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0537 (Chi-square test)

Table S74. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 155 | 131
subtype1 | 44 | 39
subtype2 | 40 | 20
subtype3 | 21 | 24
subtype4 | 29 | 37
subtype5 | 21 | 11

Figure S67. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.411 (ANOVA)

Table S75. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 74.2 (33.7)
subtype1 | 10 | 67.0 (37.4)
subtype2 | 7 | 92.9 (7.6)
subtype4 | 6 | 70.0 (34.6)
subtype5 | 3 | 63.3 (55.1)

Figure S68. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.271 (Chi-square test)

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 64 | 181 | 3 | 10 | 2 | 3 | 2 | 10 | 1 | 4
subtype1 | 1 | 13 | 60 | 0 | 3 | 1 | 2 | 0 | 3 | 0 | 0
subtype2 | 2 | 16 | 34 | 1 | 5 | 0 | 1 | 0 | 1 | 0 | 0
subtype3 | 1 | 12 | 24 | 1 | 0 | 1 | 0 | 1 | 2 | 0 | 3
subtype4 | 1 | 15 | 44 | 0 | 2 | 0 | 0 | 0 | 3 | 1 | 0
subtype5 | 1 | 8 | 19 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1

Figure S69. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.182 (Chi-square test)

Table S77. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 79 | 167 | 23 | 16
subtype1 | 20 | 51 | 7 | 5
subtype2 | 24 | 30 | 3 | 3
subtype3 | 6 | 29 | 6 | 4
subtype4 | 16 | 43 | 4 | 3
subtype5 | 13 | 14 | 3 | 1

Figure S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00208 (Chi-square test)

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

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 171 | 58 | 51
subtype1 | 44 | 19 | 19
subtype2 | 43 | 10 | 7
subtype3 | 17 | 14 | 14
subtype4 | 50 | 7 | 8
subtype5 | 17 | 8 | 3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.792 (Chi-square test)

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 199 | 16 | 64
subtype1 | 58 | 4 | 19
subtype2 | 43 | 2 | 13
subtype3 | 36 | 2 | 7
subtype4 | 42 | 5 | 17
subtype5 | 20 | 3 | 8

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.072 (Chi-square test)

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 147 | 61 | 57 | 16
subtype1 | 36 | 21 | 19 | 5
subtype2 | 37 | 10 | 8 | 2
subtype3 | 15 | 12 | 16 | 2
subtype4 | 40 | 11 | 11 | 4
subtype5 | 19 | 7 | 3 | 3

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.652 (Chi-square test)

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 17 | 269
subtype1 | 6 | 77
subtype2 | 4 | 56
subtype3 | 4 | 41
subtype4 | 2 | 64
subtype5 | 1 | 31

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0262 (Chi-square test)

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 44 | 242
subtype1 | 19 | 64
subtype2 | 11 | 49
subtype3 | 8 | 37
subtype4 | 3 | 63
subtype5 | 3 | 29

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S83. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 122 | 87 | 77

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00236 (logrank test)

Table S84. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 252 | 79 | 0.0 - 224.0 (13.4)
subtype1 | 107 | 28 | 0.0 - 224.0 (14.0)
subtype2 | 77 | 28 | 0.1 - 83.8 (15.0)
subtype3 | 68 | 23 | 0.0 - 77.9 (9.0)

Figure S76. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0518 (ANOVA)

Table S85. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 255 | 65.6 (9.6)
subtype1 | 109 | 67.3 (8.4)
subtype2 | 78 | 64.2 (9.5)
subtype3 | 68 | 64.4 (11.3)

Figure S77. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00756 (Fisher's exact test)

Table S86. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 155 | 131
subtype1 | 74 | 48
subtype2 | 35 | 52
subtype3 | 46 | 31

Figure S78. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.603 (ANOVA)

Table S87. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 74.2 (33.7)
subtype1 | 11 | 76.4 (38.3)
subtype2 | 6 | 83.3 (5.2)
subtype3 | 9 | 65.6 (39.4)

Figure S79. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.301 (Chi-square test)

Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 64 | 181 | 3 | 10 | 2 | 3 | 2 | 10 | 1 | 4
subtype1 | 4 | 29 | 70 | 3 | 7 | 0 | 1 | 2 | 4 | 0 | 2
subtype2 | 1 | 22 | 55 | 0 | 2 | 1 | 0 | 0 | 3 | 1 | 2
subtype3 | 1 | 13 | 56 | 0 | 1 | 1 | 2 | 0 | 3 | 0 | 0

Figure S80. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.369 (Chi-square test)

Table S89. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 79 | 167 | 23 | 16
subtype1 | 42 | 64 | 9 | 6
subtype2 | 18 | 58 | 6 | 5
subtype3 | 19 | 45 | 8 | 5

Figure S81. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0485 (Chi-square test)

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 171 | 58 | 51
subtype1 | 80 | 27 | 12
subtype2 | 50 | 15 | 21
subtype3 | 41 | 16 | 18

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.901 (Chi-square test)

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 199 | 16 | 64
subtype1 | 83 | 7 | 28
subtype2 | 59 | 6 | 20
subtype3 | 57 | 3 | 16

Figure S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.111 (Chi-square test)

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 147 | 61 | 57 | 16
subtype1 | 72 | 25 | 14 | 7
subtype2 | 41 | 18 | 23 | 5
subtype3 | 34 | 18 | 20 | 4

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.508 (Fisher's exact test)

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 17 | 269
subtype1 | 5 | 117
subtype2 | 6 | 81
subtype3 | 6 | 71

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0686 (Fisher's exact test)

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 44 | 242
subtype1 | 17 | 105
subtype2 | 9 | 78
subtype3 | 18 | 59

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S95. Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 111 | 113 | 60

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0944 (logrank test)

Table S96. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 250 | 79 | 0.0 - 224.0 (13.4)
subtype1 | 99 | 26 | 0.1 - 163.1 (13.2)
subtype2 | 96 | 32 | 0.0 - 83.8 (12.9)
subtype3 | 55 | 21 | 0.0 - 224.0 (14.9)

Figure S87. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.329 (ANOVA)

Table S97. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 254 | 65.4 (9.8)
subtype1 | 102 | 66.4 (9.1)
subtype2 | 101 | 65.0 (10.1)
subtype3 | 51 | 64.1 (10.4)

Figure S88. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.83 (Fisher's exact test)

Table S98. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 154 | 130
subtype1 | 61 | 50
subtype2 | 59 | 54
subtype3 | 34 | 26

Figure S89. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.919 (ANOVA)

Table S99. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 24 | 72.1 (34.3)
subtype1 | 10 | 71.0 (38.4)
subtype2 | 11 | 70.9 (35.6)
subtype3 | 3 | 80.0 (20.0)

Figure S90. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.152 (Chi-square test)

Table S100. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 64 | 181 | 3 | 10 | 2 | 2 | 1 | 10 | 1 | 4
subtype1 | 3 | 27 | 63 | 2 | 7 | 0 | 1 | 1 | 6 | 0 | 1
subtype2 | 2 | 31 | 73 | 0 | 1 | 2 | 0 | 0 | 2 | 1 | 1
subtype3 | 1 | 6 | 45 | 1 | 2 | 0 | 1 | 0 | 2 | 0 | 2

Figure S91. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.625 (Chi-square test)

Table S101. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 78 | 165 | 23 | 16
subtype1 | 35 | 59 | 9 | 7
subtype2 | 30 | 69 | 7 | 7
subtype3 | 13 | 37 | 7 | 2

Figure S92. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.753 (Chi-square test)

Table S102. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 168 | 57 | 52
subtype1 | 64 | 25 | 18
subtype2 | 69 | 22 | 20
subtype3 | 35 | 10 | 14

Figure S93. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.292 (Chi-square test)

Table S103. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 198 | 14 | 65
subtype1 | 74 | 6 | 28
subtype2 | 86 | 6 | 19
subtype3 | 38 | 2 | 18

Figure S94. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.625 (Chi-square test)

Table S104. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 147 | 59 | 58 | 15
subtype1 | 60 | 23 | 17 | 7
subtype2 | 61 | 22 | 25 | 5
subtype3 | 26 | 14 | 16 | 3

Figure S95. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.905 (Fisher's exact test)

Table S105. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 18 | 266
subtype1 | 8 | 103
subtype2 | 7 | 106
subtype3 | 3 | 57

Figure S96. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.289 (Fisher's exact test)

Table S106. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 41 | 243
subtype1 | 18 | 93
subtype2 | 12 | 101
subtype3 | 11 | 49

Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S107. Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 126 | 15 | 143

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.118 (logrank test)

Table S108. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 250 | 79 | 0.0 - 224.0 (13.4)
subtype1 | 109 | 40 | 0.0 - 83.8 (14.9)
subtype2 | 13 | 3 | 0.1 - 224.0 (5.4)
subtype3 | 128 | 36 | 0.1 - 163.1 (13.7)

Figure S98. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0539 (ANOVA)

Table S109. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 254 | 65.4 (9.8)
subtype1 | 112 | 63.8 (10.2)
subtype2 | 12 | 67.8 (8.6)
subtype3 | 130 | 66.6 (9.3)

Figure S99. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0731 (Fisher's exact test)

Table S110. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 154 | 130
subtype1 | 60 | 66
subtype2 | 11 | 4
subtype3 | 83 | 60

Figure S100. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.59 (ANOVA)

Table S111. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 24 | 72.1 (34.3)
subtype1 | 9 | 75.6 (28.8)
subtype2 | 1 | 100.0 (NA)
subtype3 | 14 | 67.9 (38.5)

Figure S101. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.442 (Chi-square test)

Table S112. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 6 | 64 | 181 | 3 | 10 | 2 | 2 | 1 | 10 | 1 | 4
subtype1 | 2 | 32 | 85 | 0 | 2 | 1 | 0 | 0 | 2 | 1 | 1
subtype2 | 0 | 1 | 11 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0
subtype3 | 4 | 31 | 85 | 2 | 7 | 1 | 2 | 1 | 7 | 0 | 3

Figure S102. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.614 (Chi-square test)

Table S113. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 78 | 165 | 23 | 16
subtype1 | 31 | 77 | 12 | 6
subtype2 | 5 | 10 | 0 | 0
subtype3 | 42 | 78 | 11 | 10

Figure S103. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.223 (Chi-square test)

Table S114. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 168 | 57 | 52
subtype1 | 76 | 21 | 27
subtype2 | 7 | 3 | 5
subtype3 | 85 | 33 | 20

Figure S104. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.4 (Chi-square test)

Table S115. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 198 | 14 | 65
subtype1 | 92 | 5 | 27
subtype2 | 9 | 2 | 2
subtype3 | 97 | 7 | 36

Figure S105. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.174 (Chi-square test)

Table S116. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 147 | 59 | 58 | 15
subtype1 | 64 | 28 | 30 | 4
subtype2 | 4 | 3 | 5 | 2
subtype3 | 79 | 28 | 23 | 9

Figure S106. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.784 (Fisher's exact test)

Table S117. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 18 | 266
subtype1 | 9 | 117
subtype2 | 0 | 15
subtype3 | 9 | 134

Figure S107. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0979 (Fisher's exact test)

Table S118. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 41 | 243
subtype1 | 13 | 113
subtype2 | 4 | 11
subtype3 | 24 | 119

Figure S108. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = LUAD.mergedcluster.txt

- Clinical data file = LUAD.clin.merged.picked.txt

- Number of patients = 292

- Number of clustering approaches = 10

- 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

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

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

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)
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