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 7 different clustering approaches and 10 clinical features across 283 patients, 10 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.

- 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death', 'GENDER', 'HISTOLOGICAL.TYPE', 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 7 different clustering approaches and 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 10 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | METHLYATION CNMF | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.337 | 0.671 | 0.00488 | 0.00509 | 2.87e-05 | 0.657 | 0.844
AGE | ANOVA | 0.477 | 0.557 | 0.448 | 0.117 | 0.197 | 0.193 | 0.0506
GENDER | Fisher's exact test | 0.272 | 0.383 | 0.014 | 2.22e-05 | 6.45e-06 | 0.0866 | 0.466
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  | 0.679 | 0.631 | 0.576 |  | 0.43
HISTOLOGICAL TYPE | Chi-square test | 0.3 | 0.274 | 0.191 | 0.112 | 0.000331 | 0.0827 | 0.0796
PATHOLOGY T | Chi-square test | 0.489 | 0.479 | 0.755 | 0.224 | 0.0898 | 0.682 | 0.545
PATHOLOGY N | Chi-square test | 0.572 | 0.651 | 0.574 | 0.0264 | 0.00175 | 0.509 | 0.599
PATHOLOGICSPREAD(M) | Chi-square test | 0.504 | 1 | 0.435 | 0.421 | 0.488 | 0.144 | 0.0639
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1 | 0.0255 | 1 | 0.661 | 0.552 | 0.821
NEOADJUVANT THERAPY | Fisher's exact test | 0.921 | 1 | 0.296 | 0.576 | 0.166 | 1 | 0.682

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 | 14 | 18
subtype1 | 2 | 3
subtype2 | 6 | 3
subtype3 | 3 | 9
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 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.921 (Fisher's exact test)

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11. 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 S12. 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 S10. 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 S13. 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 S11. 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 S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S12. 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 S15. 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 S13. 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 S16. 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 S14. 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 S17. 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 S15. 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 S18. 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 S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 1 (Fisher's exact test)

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S21. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 52 | 60 | 52 | 52

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00488 (logrank test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 188 | 60 | 0.0 - 224.0 (11.6)
subtype1 | 44 | 10 | 0.1 - 224.0 (11.6)
subtype2 | 53 | 23 | 0.1 - 88.1 (15.0)
subtype3 | 45 | 11 | 0.0 - 77.9 (5.4)
subtype4 | 46 | 16 | 0.0 - 71.5 (12.8)

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.448 (ANOVA)

Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 188 | 65.3 (9.8)
subtype1 | 42 | 67.5 (7.8)
subtype2 | 54 | 64.7 (9.1)
subtype3 | 47 | 64.8 (10.5)
subtype4 | 45 | 64.5 (11.5)

Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.014 (Fisher's exact test)

Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 101 | 115
subtype1 | 17 | 35
subtype2 | 37 | 23
subtype3 | 26 | 26
subtype4 | 21 | 31

Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.679 (ANOVA)

Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 71.1 (34.3)
subtype1 | 7 | 80.0 (35.6)
subtype2 | 4 | 62.5 (41.9)
subtype3 | 5 | 78.0 (14.8)
subtype4 | 2 | 40.0 (56.6)

Figure S22. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.191 (Chi-square test)

Table S26. Clustering Approach #3: '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 | 5 | 49 | 132 | 3 | 9 | 1 | 2 | 2 | 9 | 1 | 3
subtype1 | 3 | 9 | 28 | 1 | 6 | 0 | 1 | 2 | 1 | 0 | 1
subtype2 | 1 | 17 | 35 | 0 | 1 | 0 | 0 | 0 | 3 | 1 | 2
subtype3 | 0 | 14 | 32 | 1 | 1 | 0 | 1 | 0 | 3 | 0 | 0
subtype4 | 1 | 9 | 37 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0

Figure S23. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.755 (Chi-square test)

Table S27. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 60 | 125 | 17 | 12
subtype1 | 18 | 26 | 5 | 2
subtype2 | 14 | 38 | 4 | 4
subtype3 | 13 | 30 | 4 | 5
subtype4 | 15 | 31 | 4 | 1

Figure S24. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.574 (Chi-square test)

Table S28. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 125 | 44 | 42
subtype1 | 33 | 9 | 9
subtype2 | 38 | 11 | 10
subtype3 | 28 | 14 | 9
subtype4 | 26 | 10 | 14

Figure S25. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.435 (Chi-square test)

Table S29. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 139 | 10 | 61
subtype1 | 35 | 0 | 14
subtype2 | 41 | 4 | 14
subtype3 | 29 | 4 | 17
subtype4 | 34 | 2 | 16

Figure S26. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.0255 (Fisher's exact test)

Table S30. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 207 | 9
subtype1 | 52 | 0
subtype2 | 58 | 2
subtype3 | 51 | 1
subtype4 | 46 | 6

Figure S27. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.296 (Fisher's exact test)

Table S31. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 185 | 31
subtype1 | 41 | 11
subtype2 | 55 | 5
subtype3 | 45 | 7
subtype4 | 44 | 8

Figure S28. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S32. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 88 | 68 | 84

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00509 (logrank test)

Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 214 | 75 | 0.0 - 224.0 (15.0)
subtype1 | 77 | 19 | 0.1 - 224.0 (13.9)
subtype2 | 60 | 25 | 0.0 - 77.9 (11.0)
subtype3 | 77 | 31 | 0.0 - 88.1 (18.3)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.117 (ANOVA)

Table S34. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 214 | 65.5 (9.7)
subtype1 | 79 | 67.1 (8.5)
subtype2 | 59 | 63.7 (11.3)
subtype3 | 76 | 65.1 (9.5)

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 2.22e-05 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 110 | 130
subtype1 | 26 | 62
subtype2 | 30 | 38
subtype3 | 54 | 30

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.631 (ANOVA)

Table S36. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 69.4 (39.0)
subtype1 | 8 | 71.2 (44.2)
subtype2 | 7 | 60.0 (42.0)
subtype3 | 3 | 86.7 (5.8)

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.112 (Chi-square test)

Table S37. Clustering Approach #4: '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 | 3 | 52 | 156 | 3 | 8 | 2 | 2 | 2 | 9 | 1 | 2
subtype1 | 2 | 17 | 56 | 3 | 6 | 0 | 1 | 1 | 2 | 0 | 0
subtype2 | 0 | 11 | 51 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0
subtype3 | 1 | 24 | 49 | 0 | 0 | 1 | 0 | 1 | 5 | 1 | 2

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.224 (Chi-square test)

Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 61 | 144 | 21 | 13
subtype1 | 31 | 47 | 6 | 3
subtype2 | 14 | 42 | 7 | 5
subtype3 | 16 | 55 | 8 | 5

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0264 (Chi-square test)

Table S39. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 136 | 53 | 46
subtype1 | 60 | 15 | 10
subtype2 | 30 | 19 | 18
subtype3 | 46 | 19 | 18

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.421 (Chi-square test)

Table S40. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 176 | 12 | 46
subtype1 | 64 | 4 | 16
subtype2 | 53 | 1 | 14
subtype3 | 59 | 7 | 16

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 1 (Fisher's exact test)

Table S41. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 229 | 11
subtype1 | 84 | 4
subtype2 | 65 | 3
subtype3 | 80 | 4

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.576 (Fisher's exact test)

Table S42. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 203 | 37
subtype1 | 75 | 13
subtype2 | 55 | 13
subtype3 | 73 | 11

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S43. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6
--- | --- | --- | --- | --- | --- | ---
Number of samples | 25 | 12 | 49 | 23 | 56 | 75

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.87e-05 (logrank test)

Table S44. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 214 | 75 | 0.0 - 224.0 (15.0)
subtype1 | 25 | 15 | 0.1 - 45.2 (15.1)
subtype2 | 10 | 3 | 0.0 - 48.5 (16.2)
subtype3 | 43 | 12 | 0.1 - 83.8 (16.4)
subtype4 | 21 | 9 | 0.5 - 104.2 (32.1)
subtype5 | 48 | 18 | 0.0 - 77.9 (10.0)
subtype6 | 67 | 18 | 0.1 - 224.0 (13.7)

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.197 (ANOVA)

Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 214 | 65.5 (9.7)
subtype1 | 24 | 66.6 (9.3)
subtype2 | 10 | 68.1 (10.8)
subtype3 | 43 | 62.7 (9.6)
subtype4 | 22 | 67.9 (7.1)
subtype5 | 48 | 64.2 (11.7)
subtype6 | 67 | 66.5 (8.8)

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 6.45e-06 (Chi-square test)

Table S46. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 110 | 130
subtype1 | 15 | 10
subtype2 | 6 | 6
subtype3 | 31 | 18
subtype4 | 17 | 6
subtype5 | 24 | 32
subtype6 | 17 | 58

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.576 (ANOVA)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 69.4 (39.0)
subtype3 | 4 | 85.0 (5.8)
subtype4 | 1 | 90.0 (NA)
subtype5 | 6 | 56.7 (45.0)
subtype6 | 7 | 68.6 (47.1)

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000331 (Chi-square test)

Table S48. Clustering Approach #5: '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 | 3 | 52 | 156 | 3 | 8 | 2 | 2 | 2 | 9 | 1 | 2
subtype1 | 0 | 10 | 11 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2
subtype2 | 0 | 3 | 6 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0
subtype3 | 1 | 8 | 36 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0
subtype4 | 0 | 10 | 11 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0
subtype5 | 0 | 9 | 41 | 0 | 1 | 1 | 1 | 0 | 3 | 0 | 0
subtype6 | 2 | 12 | 51 | 1 | 6 | 0 | 1 | 1 | 1 | 0 | 0

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0898 (Chi-square test)

Table S49. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 61 | 144 | 21 | 13
subtype1 | 2 | 18 | 3 | 2
subtype2 | 3 | 5 | 3 | 1
subtype3 | 13 | 32 | 3 | 1
subtype4 | 4 | 15 | 1 | 3
subtype5 | 11 | 34 | 7 | 4
subtype6 | 28 | 40 | 4 | 2

Figure S44. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00175 (Chi-square test)

Table S50. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 136 | 53 | 46
subtype1 | 6 | 10 | 9
subtype2 | 7 | 4 | 1
subtype3 | 32 | 5 | 11
subtype4 | 17 | 5 | 1
subtype5 | 25 | 15 | 15
subtype6 | 49 | 14 | 9

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.488 (Chi-square test)

Table S51. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 176 | 12 | 46
subtype1 | 19 | 2 | 4
subtype2 | 9 | 1 | 2
subtype3 | 30 | 3 | 14
subtype4 | 19 | 2 | 2
subtype5 | 47 | 1 | 8
subtype6 | 52 | 3 | 16

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.661 (Chi-square test)

Table S52. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 229 | 11
subtype1 | 23 | 2
subtype2 | 12 | 0
subtype3 | 47 | 2
subtype4 | 23 | 0
subtype5 | 54 | 2
subtype6 | 70 | 5

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.166 (Chi-square test)

Table S53. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 203 | 37
subtype1 | 21 | 4
subtype2 | 9 | 3
subtype3 | 44 | 5
subtype4 | 23 | 0
subtype5 | 44 | 12
subtype6 | 62 | 13

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S54. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 39 | 29 | 32

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.657 (logrank test)

Table S55. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 85 | 30 | 0.0 - 85.3 (17.4)
subtype1 | 31 | 14 | 0.0 - 49.0 (25.0)
subtype2 | 25 | 7 | 0.0 - 60.0 (13.2)
subtype3 | 29 | 9 | 0.5 - 85.3 (14.1)

Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.193 (ANOVA)

Table S56. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 84 | 65.3 (9.6)
subtype1 | 35 | 64.5 (8.8)
subtype2 | 21 | 63.2 (10.7)
subtype3 | 28 | 67.9 (9.3)

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0866 (Fisher's exact test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 45 | 55
subtype1 | 16 | 23
subtype2 | 18 | 11
subtype3 | 11 | 21

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0827 (Chi-square test)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | ---
ALL | 20 | 72 | 2 | 1 | 3 | 2
subtype1 | 13 | 25 | 0 | 0 | 1 | 0
subtype2 | 3 | 23 | 1 | 0 | 0 | 2
subtype3 | 4 | 24 | 1 | 1 | 2 | 0

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.682 (Chi-square test)

Table S59. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 27 | 62 | 8 | 3
subtype1 | 10 | 24 | 3 | 2
subtype2 | 7 | 18 | 4 | 0
subtype3 | 10 | 20 | 1 | 1

Figure S53. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.509 (Chi-square test)

Table S60. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 58 | 22 | 18
subtype1 | 19 | 9 | 10
subtype2 | 18 | 7 | 4
subtype3 | 21 | 6 | 4

Figure S54. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.144 (Chi-square test)

Table S61. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 81 | 5 | 13
subtype1 | 32 | 2 | 4
subtype2 | 22 | 0 | 7
subtype3 | 27 | 3 | 2

Figure S55. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.552 (Fisher's exact test)

Table S62. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 96 | 4
subtype1 | 37 | 2
subtype2 | 29 | 0
subtype3 | 30 | 2

Figure S56. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S63. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 88 | 12
subtype1 | 34 | 5
subtype2 | 26 | 3
subtype3 | 28 | 4

Figure S57. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 40 | 31 | 29

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.844 (logrank test)

Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 85 | 30 | 0.0 - 85.3 (17.4)
subtype1 | 36 | 11 | 0.0 - 85.3 (8.4)
subtype2 | 27 | 10 | 0.6 - 76.2 (15.2)
subtype3 | 22 | 9 | 0.0 - 47.6 (27.5)

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0506 (ANOVA)

Table S66. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 84 | 65.3 (9.6)
subtype1 | 31 | 62.4 (9.5)
subtype2 | 28 | 68.4 (8.7)
subtype3 | 25 | 65.5 (9.7)

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.466 (Fisher's exact test)

Table S67. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 45 | 55
subtype1 | 20 | 20
subtype2 | 11 | 20
subtype3 | 14 | 15

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.43 (ANOVA)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 6 | 75.0 (37.3)
subtype1 | 3 | 90.0 (0.0)
subtype2 | 3 | 60.0 (52.9)

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0796 (Chi-square test)

Table S69. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | ---
ALL | 20 | 72 | 2 | 1 | 3 | 2
subtype1 | 4 | 33 | 1 | 0 | 0 | 2
subtype2 | 5 | 22 | 1 | 1 | 2 | 0
subtype3 | 11 | 17 | 0 | 0 | 1 | 0

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.545 (Chi-square test)

Table S70. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 27 | 62 | 8 | 3
subtype1 | 9 | 27 | 4 | 0
subtype2 | 10 | 19 | 1 | 1
subtype3 | 8 | 16 | 3 | 2

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.599 (Chi-square test)

Table S71. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 58 | 22 | 18
subtype1 | 23 | 10 | 7
subtype2 | 21 | 5 | 4
subtype3 | 14 | 7 | 7

Figure S64. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0639 (Chi-square test)

Table S72. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 81 | 5 | 13
subtype1 | 31 | 0 | 9
subtype2 | 27 | 3 | 1
subtype3 | 23 | 2 | 3

Figure S65. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.821 (Fisher's exact test)

Table S73. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 96 | 4
subtype1 | 39 | 1
subtype2 | 29 | 2
subtype3 | 28 | 1

Figure S66. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.682 (Fisher's exact test)

Table S74. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 88 | 12
subtype1 | 34 | 6
subtype2 | 27 | 4
subtype3 | 27 | 2

Figure S67. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = LUAD.mergedcluster.txt

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

- Number of patients = 283

- Number of clustering approaches = 7

- Number of selected clinical features = 10

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

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)
[5] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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