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 229 patients, 14 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'.

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

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

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

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

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGY.T' and '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, 14 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 | 0.0264 | 0.0613 | 0.289 | 0.504 | 0.85
AGE | ANOVA | 0.546 | 0.168 | 0.595 | 0.088 | 0.486 | 0.396 | 0.579
GENDER | Fisher's exact test | 0.382 | 0.00872 | 0.108 | 0.0354 | 0.0434 | 0.938 | 0.312
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.191 | 0.0891 | 0.192 | 0.387 | 0.324 | 0.336 | 0.463
HISTOLOGICAL TYPE | Chi-square test | 0.355 | 0.587 | 0.309 | 0.317 | 0.35 | 0.421 | 0.367
PATHOLOGY T | Chi-square test | 0.0029 | 0.000811 | 0.224 | 0.00695 | 0.0166 | 0.31 | 0.0281
PATHOLOGY N | Chi-square test | 0.818 | 0.32 | 0.0712 | 0.424 | 0.912 | 0.014 | 0.146
PATHOLOGICSPREAD(M) | Chi-square test | 0.146 | 0.323 | 0.122 | 0.53 | 0.455 | 0.0436 | 0.165
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.445 | 0.453 | 0.211 | 0.13 | 0.591 | 0.508 | 0.013
NEOADJUVANT THERAPY | Fisher's exact test | 0.734 | 0.799 | 0.756 | 0.647 | 0.575 | 0.746 | 0.646

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 | 4
--- | --- | --- | --- | ---
Number of samples | 30 | 38 | 37 | 28

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0264 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 129 | 52 | 0.4 - 122.4 (16.8)
subtype1 | 30 | 11 | 0.4 - 122.4 (14.9)
subtype2 | 37 | 13 | 0.4 - 99.2 (24.1)
subtype3 | 34 | 17 | 0.4 - 115.6 (11.0)
subtype4 | 28 | 11 | 0.4 - 119.8 (17.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.595 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 133 | 66.5 (8.3)
subtype1 | 30 | 68.0 (8.3)
subtype2 | 38 | 65.7 (7.6)
subtype3 | 37 | 65.6 (9.1)
subtype4 | 28 | 67.1 (8.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.108 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 99 | 34
subtype1 | 27 | 3
subtype2 | 28 | 10
subtype3 | 26 | 11
subtype4 | 18 | 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 = 0.192 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 22 | 24.5 (38.4)
subtype1 | 6 | 0.0 (0.0)
subtype2 | 2 | 25.0 (35.4)
subtype3 | 11 | 33.6 (46.7)
subtype4 | 3 | 40.0 (36.1)

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 = 0.309 (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 CARICNOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
--- | --- | --- | ---
ALL | 3 | 1 | 129
subtype1 | 0 | 0 | 30
subtype2 | 2 | 0 | 36
subtype3 | 0 | 0 | 37
subtype4 | 1 | 1 | 26

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.224 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 87 | 11 | 11
subtype1 | 2 | 22 | 4 | 2
subtype2 | 5 | 27 | 1 | 5
subtype3 | 9 | 23 | 4 | 1
subtype4 | 8 | 15 | 2 | 3

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0712 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 84 | 34 | 10 | 5
subtype1 | 13 | 12 | 4 | 1
subtype2 | 21 | 13 | 2 | 2
subtype3 | 30 | 5 | 2 | 0
subtype4 | 20 | 4 | 2 | 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 = 0.122 (Fisher's exact test)

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

nPatients | M0 | M1
--- | --- | ---
ALL | 126 | 3
subtype1 | 27 | 2
subtype2 | 36 | 0
subtype3 | 37 | 0
subtype4 | 26 | 1

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 = 0.211 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 132 | 1
subtype1 | 30 | 0
subtype2 | 38 | 0
subtype3 | 37 | 0
subtype4 | 27 | 1

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 = 0.756 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 121 | 12
subtype1 | 27 | 3
subtype2 | 33 | 5
subtype3 | 35 | 2
subtype4 | 26 | 2

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
--- | --- | --- | --- | ---
Number of samples | 45 | 70 | 62 | 46

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0613 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 207 | 88 | 0.1 - 173.8 (18.8)
subtype1 | 43 | 19 | 0.1 - 122.4 (14.1)
subtype2 | 65 | 25 | 0.4 - 141.3 (28.3)
subtype3 | 58 | 22 | 0.4 - 114.0 (8.3)
subtype4 | 41 | 22 | 0.6 - 173.8 (20.0)

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 = 0.088 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 215 | 67.5 (8.4)
subtype1 | 43 | 66.7 (9.1)
subtype2 | 68 | 66.6 (8.0)
subtype3 | 60 | 69.9 (7.6)
subtype4 | 44 | 66.6 (9.1)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0354 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 164 | 59
subtype1 | 38 | 7
subtype2 | 56 | 14
subtype3 | 41 | 21
subtype4 | 29 | 17

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 = 0.387 (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 | 6 | 0.0 (0.0)
subtype2 | 9 | 10.0 (20.0)
subtype3 | 16 | 24.4 (38.5)
subtype4 | 13 | 23.1 (40.5)

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 = 0.317 (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 | 215
subtype1 | 0 | 0 | 1 | 44
subtype2 | 3 | 1 | 0 | 66
subtype3 | 3 | 0 | 0 | 59
subtype4 | 0 | 0 | 0 | 46

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 = 0.00695 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 52 | 135 | 23 | 13
subtype1 | 6 | 29 | 8 | 2
subtype2 | 10 | 47 | 7 | 6
subtype3 | 21 | 35 | 1 | 5
subtype4 | 15 | 24 | 7 | 0

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 = 0.424 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 144 | 55 | 19 | 5
subtype1 | 27 | 11 | 5 | 2
subtype2 | 43 | 20 | 5 | 2
subtype3 | 40 | 18 | 3 | 1
subtype4 | 34 | 6 | 6 | 0

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 = 0.53 (Chi-square test)

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

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 205 | 4 | 10
subtype1 | 38 | 1 | 4
subtype2 | 65 | 0 | 3
subtype3 | 58 | 2 | 2
subtype4 | 44 | 1 | 1

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 = 0.13 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 217 | 6
subtype1 | 42 | 3
subtype2 | 70 | 0
subtype3 | 60 | 2
subtype4 | 45 | 1

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 = 0.647 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 202 | 21
subtype1 | 39 | 6
subtype2 | 64 | 6
subtype3 | 58 | 4
subtype4 | 41 | 5

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 | 78 | 81 | 64

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.289 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 207 | 88 | 0.1 - 173.8 (18.8)
subtype1 | 73 | 30 | 0.4 - 173.8 (11.8)
subtype2 | 75 | 31 | 0.4 - 141.3 (24.9)
subtype3 | 59 | 27 | 0.1 - 122.4 (19.0)

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 = 0.486 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 215 | 67.5 (8.4)
subtype1 | 75 | 68.4 (8.1)
subtype2 | 78 | 66.7 (8.0)
subtype3 | 62 | 67.5 (9.3)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0434 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 164 | 59
subtype1 | 50 | 28
subtype2 | 66 | 15
subtype3 | 48 | 16

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 = 0.324 (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 | 19 | 25.3 (39.2)
subtype2 | 13 | 6.9 (17.0)
subtype3 | 12 | 17.5 (36.7)

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 = 0.35 (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 | 215
subtype1 | 3 | 0 | 0 | 75
subtype2 | 3 | 1 | 0 | 77
subtype3 | 0 | 0 | 1 | 63

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 = 0.0166 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 52 | 135 | 23 | 13
subtype1 | 27 | 42 | 4 | 5
subtype2 | 14 | 54 | 7 | 6
subtype3 | 11 | 39 | 12 | 2

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 = 0.912 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 144 | 55 | 19 | 5
subtype1 | 52 | 19 | 6 | 1
subtype2 | 49 | 22 | 7 | 3
subtype3 | 43 | 14 | 6 | 1

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 = 0.455 (Chi-square test)

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

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 205 | 4 | 10
subtype1 | 74 | 2 | 2
subtype2 | 75 | 0 | 4
subtype3 | 56 | 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 = 0.591 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 217 | 6
subtype1 | 75 | 3
subtype2 | 80 | 1
subtype3 | 62 | 2

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 = 0.575 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 202 | 21
subtype1 | 69 | 9
subtype2 | 73 | 8
subtype3 | 60 | 4

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 | 75 | 84 | 43

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.504 (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 | 67 | 24 | 0.1 - 114.0 (13.1)
subtype2 | 78 | 35 | 0.4 - 173.8 (20.8)
subtype3 | 42 | 20 | 0.4 - 141.3 (21.9)

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 = 0.396 (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 | 71 | 69.0 (7.8)
subtype2 | 82 | 67.3 (9.2)
subtype3 | 42 | 69.0 (7.1)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.938 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 146 | 56
subtype1 | 53 | 22
subtype2 | 61 | 23
subtype3 | 32 | 11

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 = 0.336 (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 | 12 | 24.2 (40.1)
subtype2 | 21 | 16.2 (31.4)
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 = 0.421 (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 | 0 | 0 | 0 | 75
subtype2 | 2 | 1 | 1 | 80
subtype3 | 2 | 0 | 0 | 41

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 = 0.31 (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 | 13 | 52 | 6 | 4
subtype2 | 22 | 48 | 11 | 3
subtype3 | 9 | 26 | 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 = 0.014 (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 | 54 | 16 | 4 | 1
subtype2 | 49 | 20 | 14 | 1
subtype3 | 23 | 17 | 1 | 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 = 0.0436 (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 | 64 | 2 | 8
subtype2 | 81 | 1 | 1
subtype3 | 40 | 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 = 0.508 (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 | 74 | 1
subtype2 | 80 | 4
subtype3 | 42 | 1

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 = 0.746 (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 | 65 | 10
subtype2 | 76 | 8
subtype3 | 38 | 5

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 | 38 | 69 | 95

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.85 (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 | 37 | 19 | 0.4 - 141.3 (23.0)
subtype2 | 65 | 28 | 0.4 - 122.4 (18.3)
subtype3 | 85 | 32 | 0.1 - 173.8 (16.8)

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 = 0.579 (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 | 37 | 69.5 (7.1)
subtype2 | 68 | 68.4 (8.1)
subtype3 | 90 | 67.8 (8.9)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.312 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 146 | 56
subtype1 | 31 | 7
subtype2 | 47 | 22
subtype3 | 68 | 27

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 = 0.463 (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 | 4 | 0.0 (0.0)
subtype2 | 18 | 21.7 (37.0)
subtype3 | 17 | 14.1 (30.4)

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 = 0.367 (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 | 2 | 0 | 0 | 36
subtype2 | 2 | 0 | 0 | 67
subtype3 | 0 | 1 | 1 | 93

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 = 0.0281 (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 | 8 | 21 | 3 | 6
subtype2 | 19 | 37 | 10 | 3
subtype3 | 17 | 68 | 7 | 3

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 = 0.146 (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 | 21 | 15 | 1 | 1
subtype2 | 43 | 14 | 11 | 1
subtype3 | 62 | 24 | 7 | 2

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 = 0.165 (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 | 34 | 0 | 2
subtype2 | 67 | 0 | 1
subtype3 | 84 | 3 | 7

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 = 0.013 (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 | 37 | 1
subtype2 | 64 | 5
subtype3 | 95 | 0

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 = 0.646 (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 | 34 | 4
subtype2 | 63 | 6
subtype3 | 82 | 13

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 = 229

- 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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