Lung Squamous Cell Carcinoma: 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 278 patients, 13 significant findings detected with P value < 0.05.

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

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', and 'PATHOLOGY.T'.

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

- CNMF clustering analysis on sequencing-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death', 'AGE', and 'PATHOLOGY.T'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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, 13 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 | 0.0442 | 0.0312 | 0.239 | 0.0031 | 0.234 | 0.601 | 0.503
AGE | ANOVA | 0.546 | 0.168 | 0.354 | 0.00813 | 0.443 | 0.0431 | 0.897
GENDER | Fisher's exact test | 0.382 | 0.00872 | 0.843 | 0.225 | 0.0447 | 0.654 | 0.353
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.191 | 0.0891 | 0.533 | 0.543 | 0.429 | 0.421 | 0.4
HISTOLOGICAL TYPE | Chi-square test | 0.355 | 0.587 | 0.286 | 0.897 | 0.294 | 0.412 | 0.401
PATHOLOGY T | Chi-square test | 0.0029 | 0.000811 | 0.375 | 0.0215 | 0.364 | 0.683 | 0.329
PATHOLOGY N | Chi-square test | 0.818 | 0.32 | 0.31 | 0.673 | 0.9 | 0.0392 | 0.061
PATHOLOGICSPREAD(M) | Chi-square test | 0.146 | 0.323 | 0.53 | 0.0795 | 0.655 | 0.00673 | 0.205
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.445 | 0.453 | 0.245 | 0.399 | 0.382 | 0.656 | 0.0193
NEOADJUVANT THERAPY | Fisher's exact test | 0.734 | 0.799 | 0.668 | 0.246 | 0.958 | 0.748 | 0.571

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 | 43 | 50 | 30 | 31

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0442 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 148 | 62 | 0.4 - 173.8 (18.2)
subtype1 | 42 | 16 | 0.4 - 122.4 (19.0)
subtype2 | 48 | 19 | 0.4 - 99.2 (24.5)
subtype3 | 28 | 16 | 0.4 - 82.2 (15.9)
subtype4 | 30 | 11 | 0.4 - 173.8 (11.8)

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 = 0.546 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 152 | 66.5 (8.6)
subtype1 | 42 | 66.3 (7.6)
subtype2 | 49 | 66.6 (8.4)
subtype3 | 30 | 68.2 (8.6)
subtype4 | 31 | 65.0 (10.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.382 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 110 | 44
subtype1 | 34 | 9
subtype2 | 37 | 13
subtype3 | 19 | 11
subtype4 | 20 | 11

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

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

P value = 0.191 (ANOVA)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 24.2 (38.5)
subtype1 | 4 | 0.0 (0.0)
subtype2 | 4 | 0.0 (0.0)
subtype3 | 9 | 31.1 (46.8)
subtype4 | 9 | 38.9 (39.5)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.355 (Chi-square test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | ---
ALL | 5 | 1 | 148
subtype1 | 0 | 0 | 43
subtype2 | 3 | 0 | 47
subtype3 | 1 | 0 | 29
subtype4 | 1 | 1 | 29

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0029 (Chi-square test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 30 | 100 | 12 | 12
subtype1 | 5 | 31 | 6 | 1
subtype2 | 4 | 39 | 1 | 6
subtype3 | 11 | 16 | 2 | 1
subtype4 | 10 | 14 | 3 | 4

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.818 (Chi-square test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 96 | 40 | 13 | 5
subtype1 | 26 | 11 | 4 | 2
subtype2 | 27 | 17 | 4 | 2
subtype3 | 20 | 6 | 3 | 1
subtype4 | 23 | 6 | 2 | 0

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.146 (Fisher's exact test)

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 146 | 4
subtype1 | 39 | 2
subtype2 | 48 | 0
subtype3 | 30 | 0
subtype4 | 29 | 2

Figure S8. 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 = 0.445 (Fisher's exact test)

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 152 | 2
subtype1 | 42 | 1
subtype2 | 50 | 0
subtype3 | 29 | 1
subtype4 | 31 | 0

Figure S9. 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 = 0.734 (Fisher's exact test)

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 141 | 13
subtype1 | 39 | 4
subtype2 | 45 | 5
subtype3 | 27 | 3
subtype4 | 30 | 1

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: '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 | 28 | 54 | 72

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0312 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 148 | 62 | 0.4 - 173.8 (18.2)
subtype1 | 27 | 7 | 0.4 - 173.8 (15.6)
subtype2 | 52 | 21 | 0.4 - 99.2 (23.6)
subtype3 | 69 | 34 | 0.4 - 122.4 (18.8)

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 = 0.168 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 152 | 66.5 (8.6)
subtype1 | 28 | 63.9 (8.4)
subtype2 | 53 | 66.5 (7.9)
subtype3 | 71 | 67.5 (9.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00872 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 110 | 44
subtype1 | 13 | 15
subtype2 | 42 | 12
subtype3 | 55 | 17

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

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

P value = 0.0891 (ANOVA)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 24.2 (38.5)
subtype1 | 8 | 48.8 (42.6)
subtype2 | 5 | 10.0 (22.4)
subtype3 | 13 | 14.6 (35.7)

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.587 (Chi-square test)

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | ---
ALL | 5 | 1 | 148
subtype1 | 1 | 0 | 27
subtype2 | 3 | 0 | 51
subtype3 | 1 | 1 | 70

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000811 (Chi-square test)

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 30 | 100 | 12 | 12
subtype1 | 12 | 11 | 3 | 2
subtype2 | 4 | 42 | 1 | 7
subtype3 | 14 | 47 | 8 | 3

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test)

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 96 | 40 | 13 | 5
subtype1 | 22 | 3 | 3 | 0
subtype2 | 29 | 18 | 5 | 2
subtype3 | 45 | 19 | 5 | 3

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.323 (Fisher's exact test)

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 146 | 4
subtype1 | 27 | 1
subtype2 | 52 | 0
subtype3 | 67 | 3

Figure S18. 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 = 0.453 (Fisher's exact test)

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 152 | 2
subtype1 | 27 | 1
subtype2 | 54 | 0
subtype3 | 71 | 1

Figure S19. 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 = 0.799 (Fisher's exact test)

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 141 | 13
subtype1 | 25 | 3
subtype2 | 49 | 5
subtype3 | 67 | 5

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 55 | 51 | 38

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.239 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 131 | 57 | 0.0 - 173.8 (15.6)
subtype1 | 50 | 25 | 0.1 - 173.8 (13.2)
subtype2 | 47 | 18 | 0.2 - 141.3 (14.6)
subtype3 | 34 | 14 | 0.0 - 107.0 (16.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.354 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 136 | 68.5 (9.2)
subtype1 | 52 | 69.7 (9.7)
subtype2 | 49 | 67.1 (8.1)
subtype3 | 35 | 68.5 (9.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.843 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 104 | 40
subtype1 | 38 | 17
subtype2 | 38 | 13
subtype3 | 28 | 10

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

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

P value = 0.533 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 28 | 26.8 (39.5)
subtype1 | 5 | 42.0 (49.2)
subtype2 | 12 | 18.3 (35.6)
subtype3 | 11 | 29.1 (40.6)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.286 (Chi-square test)

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

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG SMALL CELL SQUAMOUS CELL CARCINOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | --- | ---
ALL | 4 | 1 | 1 | 138
subtype1 | 0 | 0 | 0 | 55
subtype2 | 2 | 1 | 0 | 48
subtype3 | 2 | 0 | 1 | 35

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.375 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 40 | 83 | 15 | 6
subtype1 | 14 | 33 | 6 | 2
subtype2 | 17 | 24 | 8 | 2
subtype3 | 9 | 26 | 1 | 2

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.31 (Chi-square test)

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 92 | 40 | 12
subtype1 | 33 | 14 | 8
subtype2 | 35 | 14 | 2
subtype3 | 24 | 12 | 2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.53 (Chi-square test)

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

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 119 | 1 | 22
subtype1 | 43 | 1 | 11
subtype2 | 45 | 0 | 6
subtype3 | 31 | 0 | 5

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

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

P value = 0.245 (Fisher's exact test)

Table S32. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 136 | 8
subtype1 | 52 | 3
subtype2 | 50 | 1
subtype3 | 34 | 4

Figure S29. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.668 (Fisher's exact test)

Table S33. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 128 | 16
subtype1 | 48 | 7
subtype2 | 47 | 4
subtype3 | 33 | 5

Figure S30. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S34. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
--- | --- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 22 | 20 | 55 | 40 | 34 | 36 | 4 | 9

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0031 (logrank test)

Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 204 | 85 | 0.1 - 173.8 (18.5)
subtype1 | 20 | 6 | 0.1 - 115.6 (11.9)
subtype2 | 17 | 9 | 0.8 - 60.9 (13.1)
subtype3 | 52 | 20 | 0.4 - 141.3 (23.7)
subtype4 | 37 | 17 | 0.4 - 114.0 (15.6)
subtype5 | 32 | 14 | 1.0 - 122.4 (23.6)
subtype6 | 34 | 13 | 0.4 - 173.8 (11.3)
subtype7 | 3 | 2 | 1.0 - 27.0 (4.3)
subtype8 | 9 | 4 | 0.6 - 97.9 (32.7)

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00813 (ANOVA)

Table S36. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 67.5 (8.4)
subtype1 | 22 | 65.1 (8.7)
subtype2 | 18 | 66.8 (7.9)
subtype3 | 54 | 66.0 (8.3)
subtype4 | 38 | 71.7 (6.9)
subtype5 | 32 | 68.8 (9.2)
subtype6 | 35 | 64.9 (8.8)
subtype7 | 4 | 66.2 (3.4)
subtype8 | 9 | 71.0 (6.2)

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.225 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 161 | 59
subtype1 | 17 | 5
subtype2 | 16 | 4
subtype3 | 45 | 10
subtype4 | 23 | 17
subtype5 | 26 | 8
subtype6 | 26 | 10
subtype7 | 3 | 1
subtype8 | 5 | 4

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.543 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 44 | 17.7 (33.6)
subtype1 | 3 | 6.7 (11.5)
subtype2 | 4 | 25.0 (50.0)
subtype3 | 7 | 12.9 (22.1)
subtype4 | 11 | 16.4 (36.4)
subtype5 | 7 | 0.0 (0.0)
subtype6 | 10 | 30.0 (40.3)
subtype8 | 2 | 45.0 (63.6)

Figure S34. 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 = 0.897 (Chi-square test)

Table S39. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | --- | ---
ALL | 6 | 1 | 1 | 212
subtype1 | 0 | 0 | 0 | 22
subtype2 | 0 | 0 | 0 | 20
subtype3 | 3 | 1 | 0 | 51
subtype4 | 2 | 0 | 0 | 38
subtype5 | 0 | 0 | 1 | 33
subtype6 | 1 | 0 | 0 | 35
subtype7 | 0 | 0 | 0 | 4
subtype8 | 0 | 0 | 0 | 9

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0215 (Chi-square test)

Table S40. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 51 | 133 | 23 | 13
subtype1 | 2 | 13 | 7 | 0
subtype2 | 5 | 12 | 3 | 0
subtype3 | 8 | 35 | 6 | 6
subtype4 | 11 | 25 | 1 | 3
subtype5 | 5 | 26 | 2 | 1
subtype6 | 14 | 16 | 4 | 2
subtype7 | 2 | 2 | 0 | 0
subtype8 | 4 | 4 | 0 | 1

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.673 (Chi-square test)

Table S41. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 142 | 54 | 19 | 5
subtype1 | 16 | 4 | 2 | 0
subtype2 | 14 | 4 | 1 | 1
subtype3 | 33 | 17 | 4 | 1
subtype4 | 26 | 12 | 1 | 1
subtype5 | 23 | 7 | 3 | 1
subtype6 | 21 | 8 | 7 | 0
subtype7 | 4 | 0 | 0 | 0
subtype8 | 5 | 2 | 1 | 1

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0795 (Chi-square test)

Table S42. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 203 | 4 | 9
subtype1 | 16 | 1 | 4
subtype2 | 19 | 0 | 0
subtype3 | 51 | 0 | 2
subtype4 | 40 | 0 | 0
subtype5 | 31 | 2 | 1
subtype6 | 33 | 1 | 2
subtype7 | 4 | 0 | 0
subtype8 | 9 | 0 | 0

Figure S38. 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 = 0.399 (Chi-square test)

Table S43. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 214 | 6
subtype1 | 22 | 0
subtype2 | 18 | 2
subtype3 | 55 | 0
subtype4 | 38 | 2
subtype5 | 33 | 1
subtype6 | 35 | 1
subtype7 | 4 | 0
subtype8 | 9 | 0

Figure S39. 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 = 0.246 (Chi-square test)

Table S44. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 199 | 21
subtype1 | 21 | 1
subtype2 | 18 | 2
subtype3 | 51 | 4
subtype4 | 38 | 2
subtype5 | 31 | 3
subtype6 | 28 | 8
subtype7 | 4 | 0
subtype8 | 8 | 1

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S45. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 65 | 74 | 81

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.234 (logrank test)

Table S46. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 204 | 85 | 0.1 - 173.8 (18.5)
subtype1 | 60 | 25 | 0.4 - 173.8 (8.9)
subtype2 | 68 | 28 | 0.4 - 141.3 (23.1)
subtype3 | 76 | 32 | 0.1 - 122.4 (22.3)

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.443 (ANOVA)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 67.5 (8.4)
subtype1 | 64 | 67.6 (8.6)
subtype2 | 71 | 66.5 (8.2)
subtype3 | 77 | 68.3 (8.5)

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0447 (Fisher's exact test)

Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 161 | 59
subtype1 | 40 | 25
subtype2 | 59 | 15
subtype3 | 62 | 19

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.429 (ANOVA)

Table S49. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 44 | 17.7 (33.6)
subtype1 | 17 | 24.1 (37.3)
subtype2 | 12 | 7.5 (17.6)
subtype3 | 15 | 18.7 (38.7)

Figure S44. 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 = 0.294 (Chi-square test)

Table S50. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | --- | ---
ALL | 6 | 1 | 1 | 212
subtype1 | 3 | 0 | 0 | 62
subtype2 | 3 | 1 | 0 | 70
subtype3 | 0 | 0 | 1 | 80

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.364 (Chi-square test)

Table S51. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 51 | 133 | 23 | 13
subtype1 | 21 | 33 | 6 | 5
subtype2 | 13 | 49 | 7 | 5
subtype3 | 17 | 51 | 10 | 3

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.9 (Chi-square test)

Table S52. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 142 | 54 | 19 | 5
subtype1 | 41 | 17 | 6 | 1
subtype2 | 46 | 21 | 5 | 2
subtype3 | 55 | 16 | 8 | 2

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.655 (Chi-square test)

Table S53. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 203 | 4 | 9
subtype1 | 61 | 2 | 2
subtype2 | 69 | 0 | 3
subtype3 | 73 | 2 | 4

Figure S48. 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 = 0.382 (Fisher's exact test)

Table S54. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 214 | 6
subtype1 | 64 | 1
subtype2 | 73 | 1
subtype3 | 77 | 4

Figure S49. 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 = 0.958 (Fisher's exact test)

Table S55. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 199 | 21
subtype1 | 58 | 7
subtype2 | 67 | 7
subtype3 | 74 | 7

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S56. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 84 | 73 | 45

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.601 (logrank test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 187 | 79 | 0.1 - 173.8 (18.8)
subtype1 | 81 | 34 | 0.4 - 173.8 (18.3)
subtype2 | 62 | 25 | 0.1 - 114.0 (18.4)
subtype3 | 44 | 20 | 0.4 - 141.3 (20.5)

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0431 (ANOVA)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 68.3 (8.3)
subtype1 | 83 | 66.8 (9.3)
subtype2 | 68 | 70.2 (7.1)
subtype3 | 44 | 68.2 (7.5)

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.654 (Fisher's exact test)

Table S59. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 146 | 56
subtype1 | 59 | 25
subtype2 | 52 | 21
subtype3 | 35 | 10

Figure S53. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.421 (ANOVA)

Table S60. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 39 | 16.2 (32.3)
subtype1 | 21 | 18.6 (35.0)
subtype2 | 12 | 20.0 (34.9)
subtype3 | 6 | 0.0 (0.0)

Figure S54. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.412 (Chi-square test)

Table S61. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | --- | ---
ALL | 4 | 1 | 1 | 196
subtype1 | 2 | 0 | 1 | 81
subtype2 | 0 | 1 | 0 | 72
subtype3 | 2 | 0 | 0 | 43

Figure S55. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.683 (Chi-square test)

Table S62. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 44 | 126 | 20 | 12
subtype1 | 18 | 53 | 9 | 4
subtype2 | 18 | 44 | 8 | 3
subtype3 | 8 | 29 | 3 | 5

Figure S56. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0392 (Chi-square test)

Table S63. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 126 | 53 | 19 | 4
subtype1 | 52 | 18 | 13 | 1
subtype2 | 51 | 17 | 4 | 1
subtype3 | 23 | 18 | 2 | 2

Figure S57. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.00673 (Chi-square test)

Table S64. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 185 | 3 | 10
subtype1 | 80 | 2 | 0
subtype2 | 63 | 1 | 9
subtype3 | 42 | 0 | 1

Figure S58. 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 = 0.656 (Fisher's exact test)

Table S65. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 196 | 6
subtype1 | 81 | 3
subtype2 | 72 | 1
subtype3 | 43 | 2

Figure S59. 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 = 0.748 (Fisher's exact test)

Table S66. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 179 | 23
subtype1 | 76 | 8
subtype2 | 64 | 9
subtype3 | 39 | 6

Figure S60. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S67. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 97 | 63 | 42

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.503 (logrank test)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 187 | 79 | 0.1 - 173.8 (18.8)
subtype1 | 88 | 35 | 0.1 - 173.8 (14.3)
subtype2 | 59 | 26 | 0.4 - 122.4 (22.8)
subtype3 | 40 | 18 | 0.4 - 141.3 (20.5)

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.897 (ANOVA)

Table S69. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 68.3 (8.3)
subtype1 | 93 | 68.1 (8.9)
subtype2 | 61 | 68.7 (7.8)
subtype3 | 41 | 68.1 (7.5)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.353 (Fisher's exact test)

Table S70. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 146 | 56
subtype1 | 69 | 28
subtype2 | 43 | 20
subtype3 | 34 | 8

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.4 (ANOVA)

Table S71. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 39 | 16.2 (32.3)
subtype1 | 18 | 13.3 (29.7)
subtype2 | 17 | 22.9 (37.7)
subtype3 | 4 | 0.0 (0.0)

Figure S64. 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 = 0.401 (Chi-square test)

Table S72. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | --- | ---
ALL | 4 | 1 | 1 | 196
subtype1 | 0 | 1 | 1 | 95
subtype2 | 2 | 0 | 0 | 61
subtype3 | 2 | 0 | 0 | 40

Figure S65. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.329 (Chi-square test)

Table S73. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 44 | 126 | 20 | 12
subtype1 | 17 | 66 | 9 | 5
subtype2 | 17 | 36 | 8 | 2
subtype3 | 10 | 24 | 3 | 5

Figure S66. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.061 (Chi-square test)

Table S74. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 126 | 53 | 19 | 4
subtype1 | 65 | 24 | 6 | 2
subtype2 | 39 | 12 | 11 | 1
subtype3 | 22 | 17 | 2 | 1

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

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

P value = 0.205 (Chi-square test)

Table S75. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 185 | 3 | 10
subtype1 | 86 | 3 | 7
subtype2 | 61 | 0 | 1
subtype3 | 38 | 0 | 2

Figure S68. 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 = 0.0193 (Fisher's exact test)

Table S76. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 196 | 6
subtype1 | 97 | 0
subtype2 | 59 | 4
subtype3 | 40 | 2

Figure S69. 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 = 0.571 (Fisher's exact test)

Table S77. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 179 | 23
subtype1 | 84 | 13
subtype2 | 58 | 5
subtype3 | 37 | 5

Figure S70. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = LUSC.mergedcluster.txt

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

- Number of patients = 278

- 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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