This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 279 patients, 17 significant findings detected with P value < 0.05.
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CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'PATHOLOGY.T'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', and 'PATHOLOGY.T'.
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3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'GENDER'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.
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Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death', 'AGE', and 'PATHOLOGY.T'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER'.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 17 significant findings detected.
Clinical Features |
Statistical Tests |
mRNA CNMF subtypes |
mRNA cHierClus subtypes |
CN CNMF |
METHLYATION CNMF |
RPPA CNMF subtypes |
RPPA cHierClus subtypes |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRseq CNMF subtypes |
MIRseq cHierClus subtypes |
Time to Death | logrank test | 0.0442 | 0.0312 | 0.0659 | 0.186 | 0.000862 | 0.000346 | 0.0031 | 0.234 | 0.601 | 0.503 |
AGE | ANOVA | 0.546 | 0.168 | 0.237 | 0.213 | 0.0454 | 0.185 | 0.00813 | 0.443 | 0.0431 | 0.897 |
GENDER | Fisher's exact test | 0.382 | 0.00872 | 0.00603 | 0.768 | 0.224 | 0.73 | 0.225 | 0.0447 | 0.654 | 0.353 |
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.191 | 0.0891 | 0.404 | 0.439 | 0.725 | 0.992 | 0.543 | 0.429 | 0.421 | 0.4 |
HISTOLOGICAL TYPE | Chi-square test | 0.355 | 0.587 | 0.199 | 0.22 | 0.292 | 0.198 | 0.897 | 0.294 | 0.412 | 0.401 |
PATHOLOGY T | Chi-square test | 0.0029 | 0.000811 | 0.556 | 0.314 | 0.131 | 0.252 | 0.0215 | 0.364 | 0.683 | 0.329 |
PATHOLOGY N | Chi-square test | 0.818 | 0.32 | 0.133 | 0.37 | 0.45 | 0.162 | 0.673 | 0.9 | 0.0392 | 0.061 |
PATHOLOGICSPREAD(M) | Chi-square test | 0.146 | 0.323 | 0.699 | 0.553 | 0.594 | 0.184 | 0.0795 | 0.655 | 0.00673 | 0.205 |
TUMOR STAGE | Chi-square test | 0.798 | 0.617 | 0.738 | 0.791 | 0.171 | 0.139 | 0.668 | 0.564 | 0.842 | 0.336 |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.445 | 0.453 | 0.663 | 0.581 | 0.735 | 0.71 | 0.399 | 0.382 | 0.656 | 0.0193 |
NEOADJUVANT THERAPY | Fisher's exact test | 0.734 | 0.799 | 0.623 | 0.944 | 0.524 | 1 | 0.246 | 0.958 | 0.748 | 0.571 |
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 |
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'
![](D1V1.png)
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'
![](D1V2.png)
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 | 44 | 110 |
subtype1 | 9 | 34 |
subtype2 | 13 | 37 |
subtype3 | 11 | 19 |
subtype4 | 11 | 20 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
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'
![](D1V4.png)
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'
![](D1V5.png)
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'
![](D1V6.png)
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'
![](D1V7.png)
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)'
![](D1V8.png)
P value = 0.798 (Chi-square test)
Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 81 | 34 | 34 | 4 |
subtype1 | 23 | 9 | 9 | 2 |
subtype2 | 25 | 13 | 12 | 0 |
subtype3 | 17 | 6 | 6 | 0 |
subtype4 | 16 | 6 | 7 | 2 |
Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D1V9.png)
P value = 0.445 (Fisher's exact test)
Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 2 | 152 |
subtype1 | 1 | 42 |
subtype2 | 0 | 50 |
subtype3 | 1 | 29 |
subtype4 | 0 | 31 |
Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V10.png)
P value = 0.734 (Fisher's exact test)
Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 141 |
subtype1 | 4 | 39 |
subtype2 | 5 | 45 |
subtype3 | 3 | 27 |
subtype4 | 1 | 30 |
Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D1V11.png)
Table S13. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 28 | 54 | 72 |
P value = 0.0312 (logrank test)
Table S14. 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 S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.168 (ANOVA)
Table S15. 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 S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.00872 (Fisher's exact test)
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 44 | 110 |
subtype1 | 15 | 13 |
subtype2 | 12 | 42 |
subtype3 | 17 | 55 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.0891 (ANOVA)
Table S17. 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 S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D2V4.png)
P value = 0.587 (Chi-square test)
Table S18. 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 S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D2V5.png)
P value = 0.000811 (Chi-square test)
Table S19. 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 S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D2V6.png)
P value = 0.32 (Chi-square test)
Table S20. 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 S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D2V7.png)
P value = 0.323 (Fisher's exact test)
Table S21. 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 S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D2V8.png)
P value = 0.617 (Chi-square test)
Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 81 | 34 | 34 | 4 |
subtype1 | 17 | 4 | 6 | 1 |
subtype2 | 26 | 14 | 14 | 0 |
subtype3 | 38 | 16 | 14 | 3 |
Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D2V9.png)
P value = 0.453 (Fisher's exact test)
Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 2 | 152 |
subtype1 | 1 | 27 |
subtype2 | 0 | 54 |
subtype3 | 1 | 71 |
Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V10.png)
P value = 0.799 (Fisher's exact test)
Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 141 |
subtype1 | 3 | 25 |
subtype2 | 5 | 49 |
subtype3 | 5 | 67 |
Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D2V11.png)
Table S25. Get Full Table Description of clustering approach #3: 'CN CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 101 | 77 | 100 |
P value = 0.0659 (logrank test)
Table S26. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 261 | 109 | 0.0 - 173.8 (15.8) |
subtype1 | 95 | 47 | 0.1 - 173.8 (17.9) |
subtype2 | 73 | 24 | 0.4 - 114.0 (21.1) |
subtype3 | 93 | 38 | 0.0 - 122.4 (11.8) |
Figure S23. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.237 (ANOVA)
Table S27. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 270 | 67.5 (8.8) |
subtype1 | 99 | 67.1 (9.8) |
subtype2 | 74 | 66.4 (8.1) |
subtype3 | 97 | 68.6 (8.2) |
Figure S24. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.00603 (Fisher's exact test)
Table S28. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 74 | 204 |
subtype1 | 37 | 64 |
subtype2 | 12 | 65 |
subtype3 | 25 | 75 |
Figure S25. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.404 (ANOVA)
Table S29. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 52 | 26.5 (39.1) |
subtype1 | 20 | 35.5 (43.8) |
subtype2 | 11 | 17.3 (32.9) |
subtype3 | 21 | 22.9 (37.3) |
Figure S26. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 0.199 (Chi-square test)
Table S30. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG BASALOID SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | LUNG SMALL CELL SQUAMOUS CELL CARCINOMA | LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) |
---|---|---|---|---|---|
ALL | 7 | 1 | 1 | 1 | 268 |
subtype1 | 0 | 0 | 1 | 1 | 99 |
subtype2 | 4 | 1 | 0 | 0 | 72 |
subtype3 | 3 | 0 | 0 | 0 | 97 |
Figure S27. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D3V5.png)
P value = 0.556 (Chi-square test)
Table S31. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 63 | 172 | 26 | 17 |
subtype1 | 25 | 61 | 11 | 4 |
subtype2 | 12 | 51 | 8 | 6 |
subtype3 | 26 | 60 | 7 | 7 |
Figure S28. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D3V6.png)
P value = 0.133 (Chi-square test)
Table S32. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 176 | 74 | 23 | 5 |
subtype1 | 71 | 18 | 10 | 2 |
subtype2 | 40 | 28 | 7 | 2 |
subtype3 | 65 | 28 | 6 | 1 |
Figure S29. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D3V7.png)
P value = 0.699 (Chi-square test)
Table S33. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 246 | 4 | 22 |
subtype1 | 89 | 1 | 10 |
subtype2 | 68 | 2 | 4 |
subtype3 | 89 | 1 | 8 |
Figure S30. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D3V8.png)
P value = 0.738 (Chi-square test)
Table S34. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 143 | 74 | 53 | 4 |
subtype1 | 57 | 24 | 17 | 1 |
subtype2 | 35 | 22 | 18 | 2 |
subtype3 | 51 | 28 | 18 | 1 |
Figure S31. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D3V9.png)
P value = 0.663 (Fisher's exact test)
Table S35. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 10 | 268 |
subtype1 | 3 | 98 |
subtype2 | 4 | 73 |
subtype3 | 3 | 97 |
Figure S32. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V10.png)
P value = 0.623 (Fisher's exact test)
Table S36. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 29 | 249 |
subtype1 | 12 | 89 |
subtype2 | 9 | 68 |
subtype3 | 8 | 92 |
Figure S33. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D3V11.png)
Table S37. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 57 | 54 | 33 |
P value = 0.186 (logrank test)
Table S38. Clustering Approach #4: '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 | 52 | 26 | 0.1 - 173.8 (13.2) |
subtype2 | 49 | 19 | 0.2 - 141.3 (18.8) |
subtype3 | 30 | 12 | 0.0 - 107.0 (15.7) |
Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.213 (ANOVA)
Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 68.5 (9.2) |
subtype1 | 54 | 70.2 (9.8) |
subtype2 | 51 | 67.2 (8.0) |
subtype3 | 31 | 67.7 (9.7) |
Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.768 (Fisher's exact test)
Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 40 | 104 |
subtype1 | 17 | 40 |
subtype2 | 13 | 41 |
subtype3 | 10 | 23 |
Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.439 (ANOVA)
Table S41. Clustering Approach #4: '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 | 13 | 16.9 (34.5) |
subtype3 | 10 | 32.0 (41.6) |
Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D4V4.png)
P value = 0.22 (Chi-square test)
Table S42. Clustering Approach #4: '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 | 57 |
subtype2 | 2 | 1 | 0 | 51 |
subtype3 | 2 | 0 | 1 | 30 |
Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D4V5.png)
P value = 0.314 (Chi-square test)
Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 40 | 83 | 15 | 6 |
subtype1 | 14 | 35 | 6 | 2 |
subtype2 | 19 | 25 | 8 | 2 |
subtype3 | 7 | 23 | 1 | 2 |
Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D4V6.png)
P value = 0.37 (Chi-square test)
Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 92 | 40 | 12 |
subtype1 | 34 | 15 | 8 |
subtype2 | 37 | 15 | 2 |
subtype3 | 21 | 10 | 2 |
Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D4V7.png)
P value = 0.553 (Chi-square test)
Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 119 | 1 | 22 |
subtype1 | 45 | 1 | 11 |
subtype2 | 48 | 0 | 6 |
subtype3 | 26 | 0 | 5 |
Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D4V8.png)
P value = 0.791 (Chi-square test)
Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 72 | 46 | 22 | 1 |
subtype1 | 25 | 20 | 10 | 1 |
subtype2 | 31 | 16 | 7 | 0 |
subtype3 | 16 | 10 | 5 | 0 |
Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D4V9.png)
P value = 0.581 (Fisher's exact test)
Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 8 | 136 |
subtype1 | 3 | 54 |
subtype2 | 2 | 52 |
subtype3 | 3 | 30 |
Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V10.png)
P value = 0.944 (Fisher's exact test)
Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 16 | 128 |
subtype1 | 7 | 50 |
subtype2 | 6 | 48 |
subtype3 | 3 | 30 |
Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D4V11.png)
Table S49. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 57 | 72 | 61 |
P value = 0.000862 (logrank test)
Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 177 | 72 | 0.0 - 173.8 (16.6) |
subtype1 | 54 | 15 | 0.0 - 173.8 (23.0) |
subtype2 | 69 | 30 | 0.2 - 115.6 (14.1) |
subtype3 | 54 | 27 | 0.1 - 119.8 (13.9) |
Figure S45. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.0454 (ANOVA)
Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 182 | 67.5 (9.4) |
subtype1 | 56 | 66.0 (10.4) |
subtype2 | 69 | 66.6 (8.5) |
subtype3 | 57 | 70.1 (9.2) |
Figure S46. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.224 (Fisher's exact test)
Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 48 | 142 |
subtype1 | 11 | 46 |
subtype2 | 17 | 55 |
subtype3 | 20 | 41 |
Figure S47. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.725 (ANOVA)
Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 33 | 28.8 (39.0) |
subtype1 | 7 | 38.6 (34.4) |
subtype2 | 14 | 28.6 (40.0) |
subtype3 | 12 | 23.3 (42.3) |
Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V4.png)
P value = 0.292 (Chi-square test)
Table S54. Clustering Approach #5: 'RPPA 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 | 3 | 1 | 1 | 185 |
subtype1 | 2 | 1 | 1 | 53 |
subtype2 | 0 | 0 | 0 | 72 |
subtype3 | 1 | 0 | 0 | 60 |
Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D5V5.png)
P value = 0.131 (Chi-square test)
Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 43 | 117 | 19 | 11 |
subtype1 | 15 | 33 | 3 | 6 |
subtype2 | 11 | 51 | 8 | 2 |
subtype3 | 17 | 33 | 8 | 3 |
Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D5V6.png)
P value = 0.45 (Chi-square test)
Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 121 | 50 | 15 | 4 |
subtype1 | 32 | 17 | 6 | 2 |
subtype2 | 44 | 21 | 5 | 2 |
subtype3 | 45 | 12 | 4 | 0 |
Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D5V7.png)
P value = 0.594 (Fisher's exact test)
Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | MX |
---|---|---|
ALL | 172 | 15 |
subtype1 | 51 | 6 |
subtype2 | 66 | 4 |
subtype3 | 55 | 5 |
Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D5V8.png)
P value = 0.171 (Chi-square test)
Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III |
---|---|---|---|
ALL | 96 | 55 | 36 |
subtype1 | 26 | 17 | 14 |
subtype2 | 33 | 26 | 11 |
subtype3 | 37 | 12 | 11 |
Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D5V9.png)
P value = 0.735 (Fisher's exact test)
Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 8 | 182 |
subtype1 | 3 | 54 |
subtype2 | 2 | 70 |
subtype3 | 3 | 58 |
Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V10.png)
P value = 0.524 (Fisher's exact test)
Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 23 | 167 |
subtype1 | 8 | 49 |
subtype2 | 10 | 62 |
subtype3 | 5 | 56 |
Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D5V11.png)
Table S61. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 47 | 46 | 56 | 41 |
P value = 0.000346 (logrank test)
Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 177 | 72 | 0.0 - 173.8 (16.6) |
subtype1 | 44 | 18 | 0.2 - 115.6 (14.3) |
subtype2 | 42 | 15 | 0.2 - 99.2 (23.0) |
subtype3 | 52 | 19 | 0.0 - 173.8 (17.8) |
subtype4 | 39 | 20 | 0.1 - 82.2 (8.8) |
Figure S56. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.185 (ANOVA)
Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 182 | 67.5 (9.4) |
subtype1 | 44 | 66.0 (8.0) |
subtype2 | 44 | 69.1 (8.9) |
subtype3 | 54 | 66.1 (10.4) |
subtype4 | 40 | 69.3 (9.8) |
Figure S57. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.73 (Fisher's exact test)
Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 48 | 142 |
subtype1 | 10 | 37 |
subtype2 | 14 | 32 |
subtype3 | 15 | 41 |
subtype4 | 9 | 32 |
Figure S58. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.992 (ANOVA)
Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 33 | 28.8 (39.0) |
subtype1 | 7 | 32.9 (41.1) |
subtype2 | 3 | 30.0 (52.0) |
subtype3 | 10 | 27.0 (33.7) |
subtype4 | 13 | 27.7 (43.4) |
Figure S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D6V4.png)
P value = 0.198 (Chi-square test)
Table S66. Clustering Approach #6: 'RPPA 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 | 3 | 1 | 1 | 185 |
subtype1 | 0 | 0 | 0 | 47 |
subtype2 | 0 | 0 | 0 | 46 |
subtype3 | 3 | 1 | 1 | 51 |
subtype4 | 0 | 0 | 0 | 41 |
Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D6V5.png)
P value = 0.252 (Chi-square test)
Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 43 | 117 | 19 | 11 |
subtype1 | 5 | 35 | 6 | 1 |
subtype2 | 15 | 25 | 4 | 2 |
subtype3 | 14 | 31 | 5 | 6 |
subtype4 | 9 | 26 | 4 | 2 |
Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D6V6.png)
P value = 0.162 (Chi-square test)
Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 121 | 50 | 15 | 4 |
subtype1 | 28 | 16 | 1 | 2 |
subtype2 | 31 | 9 | 4 | 2 |
subtype3 | 32 | 16 | 8 | 0 |
subtype4 | 30 | 9 | 2 | 0 |
Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D6V7.png)
P value = 0.184 (Fisher's exact test)
Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | MX |
---|---|---|
ALL | 172 | 15 |
subtype1 | 44 | 1 |
subtype2 | 43 | 3 |
subtype3 | 51 | 5 |
subtype4 | 34 | 6 |
Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D6V8.png)
P value = 0.139 (Chi-square test)
Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III |
---|---|---|---|
ALL | 96 | 55 | 36 |
subtype1 | 20 | 18 | 8 |
subtype2 | 28 | 8 | 10 |
subtype3 | 24 | 18 | 14 |
subtype4 | 24 | 11 | 4 |
Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D6V9.png)
P value = 0.71 (Fisher's exact test)
Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 8 | 182 |
subtype1 | 1 | 46 |
subtype2 | 3 | 43 |
subtype3 | 3 | 53 |
subtype4 | 1 | 40 |
Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V10.png)
P value = 1 (Fisher's exact test)
Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 23 | 167 |
subtype1 | 6 | 41 |
subtype2 | 5 | 41 |
subtype3 | 7 | 49 |
subtype4 | 5 | 36 |
Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D6V11.png)
Table S73. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Number of samples | 22 | 20 | 55 | 40 | 34 | 36 | 4 | 9 |
P value = 0.0031 (logrank test)
Table S74. Clustering Approach #7: '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 S67. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.00813 (ANOVA)
Table S75. Clustering Approach #7: '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 S68. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.225 (Chi-square test)
Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 59 | 161 |
subtype1 | 5 | 17 |
subtype2 | 4 | 16 |
subtype3 | 10 | 45 |
subtype4 | 17 | 23 |
subtype5 | 8 | 26 |
subtype6 | 10 | 26 |
subtype7 | 1 | 3 |
subtype8 | 4 | 5 |
Figure S69. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.543 (ANOVA)
Table S77. Clustering Approach #7: '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 S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V4.png)
P value = 0.897 (Chi-square test)
Table S78. Clustering Approach #7: '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 S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D7V5.png)
P value = 0.0215 (Chi-square test)
Table S79. Clustering Approach #7: '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 S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D7V6.png)
P value = 0.673 (Chi-square test)
Table S80. Clustering Approach #7: '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 S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D7V7.png)
P value = 0.0795 (Chi-square test)
Table S81. Clustering Approach #7: '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 S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D7V8.png)
P value = 0.668 (Chi-square test)
Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 114 | 54 | 46 | 4 |
subtype1 | 11 | 5 | 5 | 1 |
subtype2 | 11 | 6 | 3 | 0 |
subtype3 | 27 | 16 | 12 | 0 |
subtype4 | 21 | 11 | 7 | 0 |
subtype5 | 21 | 6 | 5 | 2 |
subtype6 | 14 | 9 | 11 | 1 |
subtype7 | 4 | 0 | 0 | 0 |
subtype8 | 5 | 1 | 3 | 0 |
Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D7V9.png)
P value = 0.399 (Chi-square test)
Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 6 | 214 |
subtype1 | 0 | 22 |
subtype2 | 2 | 18 |
subtype3 | 0 | 55 |
subtype4 | 2 | 38 |
subtype5 | 1 | 33 |
subtype6 | 1 | 35 |
subtype7 | 0 | 4 |
subtype8 | 0 | 9 |
Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V10.png)
P value = 0.246 (Chi-square test)
Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 21 | 199 |
subtype1 | 1 | 21 |
subtype2 | 2 | 18 |
subtype3 | 4 | 51 |
subtype4 | 2 | 38 |
subtype5 | 3 | 31 |
subtype6 | 8 | 28 |
subtype7 | 0 | 4 |
subtype8 | 1 | 8 |
Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D7V11.png)
Table S85. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 65 | 74 | 81 |
P value = 0.234 (logrank test)
Table S86. Clustering Approach #8: '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 S78. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.443 (ANOVA)
Table S87. Clustering Approach #8: '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 S79. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.0447 (Fisher's exact test)
Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 59 | 161 |
subtype1 | 25 | 40 |
subtype2 | 15 | 59 |
subtype3 | 19 | 62 |
Figure S80. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
P value = 0.429 (ANOVA)
Table S89. Clustering Approach #8: '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 S81. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D8V4.png)
P value = 0.294 (Chi-square test)
Table S90. Clustering Approach #8: '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 S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D8V5.png)
P value = 0.364 (Chi-square test)
Table S91. Clustering Approach #8: '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 S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D8V6.png)
P value = 0.9 (Chi-square test)
Table S92. Clustering Approach #8: '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 S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D8V7.png)
P value = 0.655 (Chi-square test)
Table S93. Clustering Approach #8: '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 S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D8V8.png)
P value = 0.564 (Chi-square test)
Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 114 | 54 | 46 | 4 |
subtype1 | 29 | 16 | 16 | 2 |
subtype2 | 38 | 21 | 15 | 0 |
subtype3 | 47 | 17 | 15 | 2 |
Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D8V9.png)
P value = 0.382 (Fisher's exact test)
Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 6 | 214 |
subtype1 | 1 | 64 |
subtype2 | 1 | 73 |
subtype3 | 4 | 77 |
Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D8V10.png)
P value = 0.958 (Fisher's exact test)
Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 21 | 199 |
subtype1 | 7 | 58 |
subtype2 | 7 | 67 |
subtype3 | 7 | 74 |
Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D8V11.png)
Table S97. Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 84 | 73 | 45 |
P value = 0.601 (logrank test)
Table S98. Clustering Approach #9: '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 S89. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.0431 (ANOVA)
Table S99. Clustering Approach #9: '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 S90. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D9V2.png)
P value = 0.654 (Fisher's exact test)
Table S100. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 56 | 146 |
subtype1 | 25 | 59 |
subtype2 | 21 | 52 |
subtype3 | 10 | 35 |
Figure S91. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D9V3.png)
P value = 0.421 (ANOVA)
Table S101. Clustering Approach #9: '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 S92. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D9V4.png)
P value = 0.412 (Chi-square test)
Table S102. Clustering Approach #9: '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 S93. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D9V5.png)
P value = 0.683 (Chi-square test)
Table S103. Clustering Approach #9: '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 S94. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D9V6.png)
P value = 0.0392 (Chi-square test)
Table S104. Clustering Approach #9: '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 S95. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D9V7.png)
P value = 0.00673 (Chi-square test)
Table S105. Clustering Approach #9: '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 S96. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D9V8.png)
P value = 0.842 (Chi-square test)
Table S106. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 104 | 51 | 43 | 3 |
subtype1 | 44 | 18 | 20 | 2 |
subtype2 | 38 | 20 | 13 | 1 |
subtype3 | 22 | 13 | 10 | 0 |
Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D9V9.png)
P value = 0.656 (Fisher's exact test)
Table S107. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 6 | 196 |
subtype1 | 3 | 81 |
subtype2 | 1 | 72 |
subtype3 | 2 | 43 |
Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D9V10.png)
P value = 0.748 (Fisher's exact test)
Table S108. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 23 | 179 |
subtype1 | 8 | 76 |
subtype2 | 9 | 64 |
subtype3 | 6 | 39 |
Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D9V11.png)
Table S109. Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 97 | 63 | 42 |
P value = 0.503 (logrank test)
Table S110. Clustering Approach #10: '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 S100. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.897 (ANOVA)
Table S111. Clustering Approach #10: '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 S101. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D10V2.png)
P value = 0.353 (Fisher's exact test)
Table S112. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 56 | 146 |
subtype1 | 28 | 69 |
subtype2 | 20 | 43 |
subtype3 | 8 | 34 |
Figure S102. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D10V3.png)
P value = 0.4 (ANOVA)
Table S113. Clustering Approach #10: '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 S103. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D10V4.png)
P value = 0.401 (Chi-square test)
Table S114. Clustering Approach #10: '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 S104. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D10V5.png)
P value = 0.329 (Chi-square test)
Table S115. Clustering Approach #10: '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 S105. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D10V6.png)
P value = 0.061 (Chi-square test)
Table S116. Clustering Approach #10: '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 S106. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D10V7.png)
P value = 0.205 (Chi-square test)
Table S117. Clustering Approach #10: '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 S107. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D10V8.png)
P value = 0.336 (Chi-square test)
Table S118. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 104 | 51 | 43 | 3 |
subtype1 | 52 | 26 | 16 | 3 |
subtype2 | 31 | 13 | 18 | 0 |
subtype3 | 21 | 12 | 9 | 0 |
Figure S108. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'
![](D10V9.png)
P value = 0.0193 (Fisher's exact test)
Table S119. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 6 | 196 |
subtype1 | 0 | 97 |
subtype2 | 4 | 59 |
subtype3 | 2 | 40 |
Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D10V10.png)
P value = 0.571 (Fisher's exact test)
Table S120. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 23 | 179 |
subtype1 | 13 | 84 |
subtype2 | 5 | 58 |
subtype3 | 5 | 37 |
Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'
![](D10V11.png)
-
Cluster data file = LUSC.mergedcluster.txt
-
Clinical data file = LUSC.clin.merged.picked.txt
-
Number of patients = 279
-
Number of clustering approaches = 10
-
Number of selected clinical features = 11
-
Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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
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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.