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 164 patients, 6 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 do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', and 'PATHOLOGY.N'.

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

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.

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, 6 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 | 0.257 | 0.00518 | 0.08 | 0.703 | 0.585
AGE | ANOVA | 0.477 | 0.557 | 0.523 | 0.295 | 0.0861 | 0.248 | 0.0645
GENDER | Fisher's exact test | 0.272 | 0.383 | 0.405 | 0.0255 | 0.0538 | 0.437 | 0.371
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  | 0.207 |  |  |  | 0.43
HISTOLOGICAL TYPE | Chi-square test | 0.3 | 0.274 | 0.252 | 0.24 | 0.402 | 0.0393 | 0.0939
PATHOLOGY T | Chi-square test | 0.489 | 0.479 | 0.477 | 0.0399 | 0.213 | 0.182 | 0.674
PATHOLOGY N | Chi-square test | 0.572 | 0.651 | 0.899 | 0.0177 | 0.532 | 0.587 | 0.617
PATHOLOGICSPREAD(M) | Chi-square test | 0.504 | 1 | 0.825 | 0.606 | 0.475 | 0.104 | 0.0218
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1 | 0.505 | 0.866 | 0.949 | 0.458 | 1
NEOADJUVANT THERAPY | Fisher's exact test | 0.921 | 1 | 0.0871 | 0.667 | 0.403 | 0.686 | 0.629

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 | 27 | 14 | 10 | 15

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.257 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 61 | 21 | 0.5 - 85.3 (19.1)
subtype1 | 25 | 8 | 0.5 - 85.3 (8.8)
subtype2 | 12 | 6 | 4.0 - 56.8 (23.7)
subtype3 | 10 | 4 | 2.0 - 46.7 (36.5)
subtype4 | 14 | 3 | 0.5 - 47.6 (19.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.523 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 65.4 (9.6)
subtype1 | 27 | 66.6 (9.5)
subtype2 | 13 | 64.5 (12.5)
subtype3 | 9 | 67.8 (6.4)
subtype4 | 15 | 62.7 (8.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.405 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 30 | 36
subtype1 | 10 | 17
subtype2 | 9 | 5
subtype3 | 5 | 5
subtype4 | 6 | 9

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

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

P value = 0.207 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 7 | 80.0 (36.1)
subtype2 | 4 | 65.0 (43.6)
subtype4 | 3 | 100.0 (0.0)

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 = 0.252 (Chi-square test)

Table S26. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA
--- | --- | --- | --- | --- | ---
ALL | 15 | 48 | 1 | 1 | 1
subtype1 | 5 | 22 | 0 | 0 | 0
subtype2 | 5 | 8 | 1 | 0 | 0
subtype3 | 2 | 7 | 0 | 0 | 1
subtype4 | 3 | 11 | 0 | 1 | 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 = 0.477 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 16 | 41 | 6 | 3
subtype1 | 6 | 18 | 3 | 0
subtype2 | 5 | 6 | 1 | 2
subtype3 | 2 | 8 | 0 | 0
subtype4 | 3 | 9 | 2 | 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 = 0.899 (Chi-square test)

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

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 39 | 15 | 10
subtype1 | 15 | 6 | 4
subtype2 | 7 | 4 | 3
subtype3 | 6 | 3 | 1
subtype4 | 11 | 2 | 2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.825 (Fisher's exact test)

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

nPatients | M0 | M1
--- | --- | ---
ALL | 59 | 6
subtype1 | 24 | 3
subtype2 | 13 | 1
subtype3 | 9 | 0
subtype4 | 13 | 2

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 = 0.505 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 62 | 4
subtype1 | 25 | 2
subtype2 | 14 | 0
subtype3 | 10 | 0
subtype4 | 13 | 2

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 = 0.0871 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 54 | 12
subtype1 | 21 | 6
subtype2 | 14 | 0
subtype3 | 9 | 1
subtype4 | 10 | 5

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 | 4
--- | --- | --- | --- | ---
Number of samples | 43 | 36 | 27 | 23

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00518 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 115 | 29 | 0.0 - 85.3 (13.2)
subtype1 | 38 | 4 | 0.1 - 85.3 (11.7)
subtype2 | 33 | 12 | 0.0 - 56.8 (12.2)
subtype3 | 24 | 6 | 0.1 - 83.8 (23.5)
subtype4 | 20 | 7 | 0.0 - 45.2 (8.9)

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 = 0.295 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 118 | 65.7 (9.8)
subtype1 | 38 | 67.2 (8.3)
subtype2 | 33 | 64.4 (11.9)
subtype3 | 25 | 63.4 (10.0)
subtype4 | 22 | 67.6 (7.9)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0255 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 54 | 75
subtype1 | 12 | 31
subtype2 | 13 | 23
subtype3 | 16 | 11
subtype4 | 13 | 10

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.24 (Chi-square test)

Table S36. 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 MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 2 | 28 | 86 | 1 | 3 | 2 | 2 | 3 | 1 | 1
subtype1 | 1 | 6 | 32 | 1 | 2 | 0 | 1 | 0 | 0 | 0
subtype2 | 0 | 6 | 28 | 0 | 0 | 1 | 0 | 1 | 0 | 0
subtype3 | 1 | 9 | 14 | 0 | 1 | 1 | 0 | 0 | 1 | 0
subtype4 | 0 | 7 | 12 | 0 | 0 | 0 | 1 | 2 | 0 | 1

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0399 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 35 | 77 | 11 | 5
subtype1 | 16 | 25 | 1 | 0
subtype2 | 9 | 20 | 4 | 3
subtype3 | 7 | 19 | 1 | 0
subtype4 | 3 | 13 | 5 | 2

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0177 (Chi-square test)

Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 80 | 26 | 20
subtype1 | 30 | 6 | 5
subtype2 | 20 | 10 | 5
subtype3 | 22 | 2 | 3
subtype4 | 8 | 8 | 7

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.606 (Chi-square test)

Table S39. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 97 | 6 | 24
subtype1 | 32 | 2 | 7
subtype2 | 29 | 0 | 7
subtype3 | 18 | 2 | 7
subtype4 | 18 | 2 | 3

Figure S35. 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 = 0.866 (Fisher's exact test)

Table S40. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 122 | 7
subtype1 | 41 | 2
subtype2 | 34 | 2
subtype3 | 26 | 1
subtype4 | 21 | 2

Figure S36. 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 = 0.667 (Fisher's exact test)

Table S41. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 110 | 19
subtype1 | 38 | 5
subtype2 | 30 | 6
subtype3 | 24 | 3
subtype4 | 18 | 5

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S42. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 27 | 47 | 33 | 22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.08 (logrank test)

Table S43. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 115 | 29 | 0.0 - 85.3 (13.2)
subtype1 | 25 | 6 | 0.0 - 83.8 (23.1)
subtype2 | 42 | 8 | 0.1 - 85.3 (14.5)
subtype3 | 30 | 10 | 0.0 - 56.8 (8.2)
subtype4 | 18 | 5 | 0.1 - 45.2 (8.9)

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0861 (ANOVA)

Table S44. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 118 | 65.7 (9.8)
subtype1 | 25 | 62.8 (10.1)
subtype2 | 40 | 66.8 (7.9)
subtype3 | 32 | 64.2 (12.1)
subtype4 | 21 | 69.4 (7.4)

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0538 (Fisher's exact test)

Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 54 | 75
subtype1 | 16 | 11
subtype2 | 15 | 32
subtype3 | 11 | 22
subtype4 | 12 | 10

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.402 (Chi-square test)

Table S46. 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 MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ALL | 2 | 28 | 86 | 1 | 3 | 2 | 2 | 3 | 1 | 1
subtype1 | 1 | 7 | 16 | 0 | 1 | 1 | 0 | 0 | 1 | 0
subtype2 | 1 | 6 | 34 | 1 | 2 | 1 | 1 | 1 | 0 | 0
subtype3 | 0 | 6 | 26 | 0 | 0 | 0 | 0 | 1 | 0 | 0
subtype4 | 0 | 9 | 10 | 0 | 0 | 0 | 1 | 1 | 0 | 1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.213 (Chi-square test)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 35 | 77 | 11 | 5
subtype1 | 6 | 20 | 1 | 0
subtype2 | 17 | 26 | 2 | 1
subtype3 | 8 | 18 | 4 | 3
subtype4 | 4 | 13 | 4 | 1

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.532 (Chi-square test)

Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 80 | 26 | 20
subtype1 | 20 | 3 | 4
subtype2 | 31 | 9 | 5
subtype3 | 17 | 9 | 6
subtype4 | 12 | 5 | 5

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.475 (Chi-square test)

Table S49. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 97 | 6 | 24
subtype1 | 18 | 2 | 7
subtype2 | 35 | 3 | 7
subtype3 | 25 | 0 | 8
subtype4 | 19 | 1 | 2

Figure S44. 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 = 0.949 (Fisher's exact test)

Table S50. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 122 | 7
subtype1 | 25 | 2
subtype2 | 45 | 2
subtype3 | 31 | 2
subtype4 | 21 | 1

Figure S45. 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 = 0.403 (Fisher's exact test)

Table S51. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 110 | 19
subtype1 | 23 | 4
subtype2 | 43 | 4
subtype3 | 26 | 7
subtype4 | 18 | 4

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S52. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 35 | 31 | 29

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.703 (logrank test)

Table S53. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 27 | 0.0 - 76.2 (15.5)
subtype1 | 28 | 10 | 0.1 - 49.0 (21.9)
subtype2 | 26 | 9 | 0.0 - 60.0 (10.8)
subtype3 | 26 | 8 | 0.5 - 76.2 (13.6)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.248 (ANOVA)

Table S54. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 65.2 (9.7)
subtype1 | 32 | 64.7 (9.6)
subtype2 | 22 | 63.1 (10.4)
subtype3 | 25 | 67.7 (9.0)

Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.437 (Fisher's exact test)

Table S55. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 43 | 52
subtype1 | 15 | 20
subtype2 | 17 | 14
subtype3 | 11 | 18

Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0393 (Chi-square test)

Table S56. 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 | 19 | 68 | 2 | 1 | 3 | 2
subtype1 | 13 | 21 | 0 | 0 | 1 | 0
subtype2 | 3 | 25 | 1 | 0 | 0 | 2
subtype3 | 3 | 22 | 1 | 1 | 2 | 0

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.182 (Chi-square test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 27 | 59 | 6 | 3
subtype1 | 9 | 22 | 1 | 3
subtype2 | 8 | 19 | 4 | 0
subtype3 | 10 | 18 | 1 | 0

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.587 (Chi-square test)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 55 | 22 | 16
subtype1 | 19 | 9 | 6
subtype2 | 16 | 9 | 6
subtype3 | 20 | 4 | 4

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.104 (Chi-square test)

Table S59. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 76 | 5 | 13
subtype1 | 28 | 3 | 3
subtype2 | 23 | 0 | 8
subtype3 | 25 | 2 | 2

Figure S53. 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 = 0.458 (Fisher's exact test)

Table S60. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 91 | 4
subtype1 | 33 | 2
subtype2 | 31 | 0
subtype3 | 27 | 2

Figure S54. 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 = 0.686 (Fisher's exact test)

Table S61. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 83 | 12
subtype1 | 29 | 6
subtype2 | 28 | 3
subtype3 | 26 | 3

Figure S55. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S62. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 38 | 32

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.585 (logrank test)

Table S63. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 27 | 0.0 - 76.2 (15.5)
subtype1 | 19 | 7 | 0.0 - 47.0 (25.0)
subtype2 | 33 | 10 | 0.5 - 76.2 (14.1)
subtype3 | 28 | 10 | 0.0 - 60.0 (10.8)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0645 (ANOVA)

Table S64. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 65.2 (9.7)
subtype1 | 22 | 64.8 (10.0)
subtype2 | 33 | 67.9 (8.7)
subtype3 | 24 | 61.9 (10.0)

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.371 (Fisher's exact test)

Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 43 | 52
subtype1 | 12 | 13
subtype2 | 14 | 24
subtype3 | 17 | 15

Figure S58. 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 S66. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 6 | 75.0 (37.3)
subtype2 | 3 | 60.0 (52.9)
subtype3 | 3 | 90.0 (0.0)

Figure S59. 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 = 0.0939 (Chi-square test)

Table S67. 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 | 19 | 68 | 2 | 1 | 3 | 2
subtype1 | 10 | 14 | 0 | 0 | 1 | 0
subtype2 | 6 | 28 | 1 | 1 | 2 | 0
subtype3 | 3 | 26 | 1 | 0 | 0 | 2

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.674 (Chi-square test)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 27 | 59 | 6 | 3
subtype1 | 7 | 15 | 1 | 2
subtype2 | 12 | 23 | 2 | 1
subtype3 | 8 | 21 | 3 | 0

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.617 (Chi-square test)

Table S69. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2+N3
--- | --- | --- | ---
ALL | 55 | 22 | 16
subtype1 | 12 | 7 | 5
subtype2 | 24 | 9 | 4
subtype3 | 19 | 6 | 7

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

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

P value = 0.0218 (Chi-square test)

Table S70. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 76 | 5 | 13
subtype1 | 19 | 2 | 3
subtype2 | 34 | 3 | 1
subtype3 | 23 | 0 | 9

Figure S63. 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 = 1 (Fisher's exact test)

Table S71. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 91 | 4
subtype1 | 24 | 1
subtype2 | 36 | 2
subtype3 | 31 | 1

Figure S64. 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 = 0.629 (Fisher's exact test)

Table S72. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 83 | 12
subtype1 | 23 | 2
subtype2 | 32 | 6
subtype3 | 28 | 4

Figure S65. 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 = 164

- 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. Location of data archives could not be determined.

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)
Meta

- Maintainer = TCGA GDAC Team

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