This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 12 different clustering approaches and 15 clinical features across 519 patients, 58 significant findings detected with P value < 0.05 and Q value < 0.25.
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CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGIC_STAGE'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'RACE'.
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4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'GENDER', and 'NUMBER_PACK_YEARS_SMOKED'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'GENDER', 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', and 'ETHNICITY'.
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CNMF clustering analysis on RPPA data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'RESIDUAL_TUMOR'.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'HISTOLOGICAL_TYPE', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'ETHNICITY'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'ETHNICITY'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_PACK_YEARS_SMOKED'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 58 significant findings detected.
Clinical Features |
Statistical Tests |
mRNA CNMF subtypes |
mRNA cHierClus subtypes |
Copy Number Ratio CNMF subtypes |
METHLYATION CNMF |
RPPA CNMF subtypes |
RPPA cHierClus subtypes |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
MIRseq Mature CNMF subtypes |
MIRseq Mature cHierClus subtypes |
Time to Death | logrank test |
0.944 (1.00) |
0.912 (0.983) |
0.0133 (0.0601) |
0.131 (0.314) |
0.997 (1.00) |
0.29 (0.522) |
0.00175 (0.0131) |
2.96e-08 (5.32e-06) |
0.0533 (0.163) |
0.818 (0.92) |
0.177 (0.394) |
0.0556 (0.167) |
YEARS TO BIRTH | Kruskal-Wallis (anova) |
0.65 (0.8) |
0.513 (0.684) |
0.00156 (0.0128) |
0.206 (0.418) |
0.00173 (0.0131) |
0.092 (0.254) |
0.0479 (0.15) |
0.000674 (0.00933) |
0.0102 (0.0469) |
0.00155 (0.0128) |
0.657 (0.8) |
2.92e-06 (0.000263) |
PATHOLOGIC STAGE | Fisher's exact test |
0.0454 (0.149) |
0.0363 (0.128) |
0.12 (0.292) |
0.00037 (0.00605) |
0.333 (0.576) |
0.663 (0.8) |
0.00325 (0.0196) |
0.0028 (0.0187) |
0.00182 (0.0131) |
0.659 (0.8) |
0.432 (0.668) |
0.492 (0.684) |
PATHOLOGY T STAGE | Fisher's exact test |
0.512 (0.684) |
0.276 (0.504) |
0.115 (0.286) |
0.00102 (0.0115) |
0.205 (0.418) |
0.105 (0.267) |
0.00156 (0.0128) |
0.00114 (0.0121) |
0.0176 (0.0736) |
0.133 (0.316) |
0.0152 (0.0667) |
0.00927 (0.0451) |
PATHOLOGY N STAGE | Fisher's exact test |
0.482 (0.684) |
0.452 (0.668) |
0.249 (0.477) |
0.0623 (0.184) |
0.434 (0.668) |
0.483 (0.684) |
0.0266 (0.103) |
0.00307 (0.0196) |
0.276 (0.504) |
0.716 (0.837) |
0.188 (0.399) |
0.0721 (0.209) |
PATHOLOGY M STAGE | Fisher's exact test |
0.502 (0.684) |
0.492 (0.684) |
0.187 (0.399) |
0.26 (0.493) |
0.0484 (0.15) |
0.791 (0.901) |
0.445 (0.668) |
0.507 (0.684) |
0.546 (0.712) |
0.856 (0.939) |
0.435 (0.668) |
0.577 (0.737) |
GENDER | Fisher's exact test |
0.273 (0.504) |
0.669 (0.803) |
0.0189 (0.0775) |
2e-05 (6e-04) |
0.157 (0.362) |
0.539 (0.708) |
1e-05 (0.00045) |
2e-05 (6e-04) |
0.476 (0.684) |
0.00477 (0.0268) |
0.959 (1.00) |
0.00029 (0.00522) |
RADIATION THERAPY | Fisher's exact test |
1 (1.00) |
1 (1.00) |
0.592 (0.75) |
0.0322 (0.118) |
0.202 (0.418) |
0.105 (0.267) |
0.0268 (0.103) |
0.0234 (0.0935) |
0.241 (0.472) |
0.69 (0.817) |
0.829 (0.921) |
0.187 (0.399) |
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) |
0.359 (0.604) |
0.402 (0.64) |
0.306 (0.546) |
0.824 (0.921) |
0.193 (0.404) |
0.384 (0.623) |
0.277 (0.504) |
0.651 (0.8) |
0.118 (0.292) |
0.835 (0.922) |
||
HISTOLOGICAL TYPE | Fisher's exact test |
0.431 (0.668) |
0.396 (0.636) |
0.382 (0.623) |
0.14 (0.326) |
0.00724 (0.0372) |
0.00138 (0.0128) |
1e-05 (0.00045) |
0.00013 (0.0026) |
0.00096 (0.0115) |
0.00123 (0.0123) |
4e-05 (9e-04) |
3e-05 (0.000771) |
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) |
0.46 (0.668) |
0.362 (0.604) |
0.0303 (0.114) |
0.00402 (0.0233) |
0.78 (0.895) |
0.982 (1.00) |
0.0931 (0.254) |
0.0416 (0.141) |
0.0797 (0.224) |
0.0407 (0.141) |
0.00889 (0.0444) |
0.224 (0.442) |
YEAR OF TOBACCO SMOKING ONSET | Kruskal-Wallis (anova) |
0.715 (0.837) |
0.676 (0.806) |
0.173 (0.388) |
0.888 (0.963) |
0.000782 (0.0101) |
0.00327 (0.0196) |
0.355 (0.604) |
0.000498 (0.00747) |
0.017 (0.073) |
0.00525 (0.0286) |
0.506 (0.684) |
0.00232 (0.0161) |
RESIDUAL TUMOR | Fisher's exact test |
0.535 (0.708) |
0.458 (0.668) |
0.878 (0.957) |
0.8 (0.906) |
0.00716 (0.0372) |
0.776 (0.895) |
0.645 (0.8) |
0.244 (0.472) |
0.45 (0.668) |
0.953 (1.00) |
0.161 (0.366) |
0.0973 (0.261) |
RACE | Fisher's exact test |
0.0784 (0.224) |
0.00957 (0.0453) |
0.561 (0.721) |
0.183 (0.399) |
0.332 (0.576) |
0.363 (0.604) |
0.972 (1.00) |
0.601 (0.757) |
0.44 (0.668) |
0.317 (0.56) |
0.949 (1.00) |
0.102 (0.267) |
ETHNICITY | Fisher's exact test |
1 (1.00) |
1 (1.00) |
0.221 (0.442) |
0.0463 (0.149) |
0.55 (0.713) |
0.0458 (0.149) |
0.496 (0.684) |
0.0361 (0.128) |
0.369 (0.609) |
0.758 (0.88) |
0.101 (0.267) |
0.455 (0.668) |
Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 5 | 9 | 12 | 6 |
P value = 0.944 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 7 | 0.5 - 56.8 (34.1) |
subtype1 | 4 | 1 | 6.0 - 48.6 (38.2) |
subtype2 | 9 | 2 | 4.0 - 56.8 (38.2) |
subtype3 | 12 | 2 | 0.5 - 44.9 (22.7) |
subtype4 | 6 | 2 | 20.1 - 45.2 (30.9) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.65 (Kruskal-Wallis (anova)), Q value = 0.8
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 65.7 (10.8) |
subtype1 | 4 | 58.5 (15.5) |
subtype2 | 9 | 65.0 (9.1) |
subtype3 | 12 | 67.1 (11.1) |
subtype4 | 5 | 69.4 (9.0) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D1V2.png)
P value = 0.0454 (Fisher's exact test), Q value = 0.15
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IA | STAGE IB | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IV |
---|---|---|---|---|---|---|
ALL | 12 | 11 | 1 | 3 | 3 | 2 |
subtype1 | 3 | 0 | 0 | 1 | 0 | 1 |
subtype2 | 4 | 4 | 0 | 0 | 1 | 0 |
subtype3 | 3 | 7 | 0 | 1 | 0 | 1 |
subtype4 | 2 | 0 | 1 | 1 | 2 | 0 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D1V3.png)
P value = 0.512 (Fisher's exact test), Q value = 0.68
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 12 | 19 | 1 |
subtype1 | 3 | 2 | 0 |
subtype2 | 4 | 4 | 1 |
subtype3 | 4 | 8 | 0 |
subtype4 | 1 | 5 | 0 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D1V4.png)
P value = 0.482 (Fisher's exact test), Q value = 0.68
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 23 | 4 | 4 |
subtype1 | 3 | 1 | 1 |
subtype2 | 8 | 1 | 0 |
subtype3 | 9 | 1 | 1 |
subtype4 | 3 | 1 | 2 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D1V5.png)
P value = 0.502 (Fisher's exact test), Q value = 0.68
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 30 | 2 |
subtype1 | 4 | 1 |
subtype2 | 9 | 0 |
subtype3 | 11 | 1 |
subtype4 | 6 | 0 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D1V6.png)
P value = 0.273 (Fisher's exact test), Q value = 0.5
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 18 | 14 |
subtype1 | 3 | 2 |
subtype2 | 3 | 6 |
subtype3 | 9 | 3 |
subtype4 | 3 | 3 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D1V7.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 18 | 1 |
subtype1 | 3 | 0 |
subtype2 | 6 | 0 |
subtype3 | 6 | 1 |
subtype4 | 3 | 0 |
Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D1V8.png)
P value = 0.431 (Fisher's exact test), Q value = 0.67
Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA |
---|---|---|---|
ALL | 1 | 30 | 1 |
subtype1 | 0 | 4 | 1 |
subtype2 | 0 | 9 | 0 |
subtype3 | 1 | 11 | 0 |
subtype4 | 0 | 6 | 0 |
Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D1V10.png)
P value = 0.46 (Kruskal-Wallis (anova)), Q value = 0.67
Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 20 | 41.1 (15.0) |
subtype1 | 2 | 29.0 (12.7) |
subtype2 | 9 | 47.0 (15.5) |
subtype3 | 6 | 37.0 (13.1) |
subtype4 | 3 | 40.0 (17.3) |
Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D1V11.png)
P value = 0.715 (Kruskal-Wallis (anova)), Q value = 0.84
Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 1968.5 (11.4) |
subtype1 | 2 | 1977.0 (18.4) |
subtype2 | 6 | 1971.2 (14.2) |
subtype3 | 6 | 1965.8 (8.2) |
subtype4 | 5 | 1965.2 (9.8) |
Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D1V12.png)
P value = 0.535 (Fisher's exact test), Q value = 0.71
Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R2 | RX |
---|---|---|---|
ALL | 26 | 1 | 2 |
subtype1 | 3 | 0 | 1 |
subtype2 | 7 | 1 | 0 |
subtype3 | 10 | 0 | 1 |
subtype4 | 6 | 0 | 0 |
Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D1V13.png)
P value = 0.0784 (Fisher's exact test), Q value = 0.22
Table S14. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 2 | 1 | 26 |
subtype1 | 1 | 0 | 3 |
subtype2 | 0 | 0 | 7 |
subtype3 | 0 | 0 | 12 |
subtype4 | 1 | 1 | 4 |
Figure S13. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'
![](D1V14.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S15. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 1 | 28 |
subtype1 | 0 | 4 |
subtype2 | 0 | 7 |
subtype3 | 1 | 11 |
subtype4 | 0 | 6 |
Figure S14. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D1V15.png)
Table S16. Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 14 | 11 | 7 |
P value = 0.912 (logrank test), Q value = 0.98
Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 7 | 0.5 - 56.8 (34.1) |
subtype1 | 13 | 2 | 0.5 - 47.0 (26.6) |
subtype2 | 11 | 3 | 4.0 - 56.8 (34.1) |
subtype3 | 7 | 2 | 20.1 - 48.6 (38.7) |
Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.513 (Kruskal-Wallis (anova)), Q value = 0.68
Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 65.7 (10.8) |
subtype1 | 14 | 66.7 (10.5) |
subtype2 | 11 | 62.7 (12.0) |
subtype3 | 5 | 69.4 (9.0) |
Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D2V2.png)
P value = 0.0363 (Fisher's exact test), Q value = 0.13
Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE IA | STAGE IB | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IV |
---|---|---|---|---|---|---|
ALL | 12 | 11 | 1 | 3 | 3 | 2 |
subtype1 | 3 | 8 | 0 | 1 | 0 | 2 |
subtype2 | 6 | 3 | 0 | 1 | 1 | 0 |
subtype3 | 3 | 0 | 1 | 1 | 2 | 0 |
Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D2V3.png)
P value = 0.276 (Fisher's exact test), Q value = 0.5
Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 12 | 19 | 1 |
subtype1 | 4 | 10 | 0 |
subtype2 | 6 | 4 | 1 |
subtype3 | 2 | 5 | 0 |
Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D2V4.png)
P value = 0.452 (Fisher's exact test), Q value = 0.67
Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 23 | 4 | 4 |
subtype1 | 10 | 1 | 2 |
subtype2 | 9 | 2 | 0 |
subtype3 | 4 | 1 | 2 |
Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D2V5.png)
P value = 0.492 (Fisher's exact test), Q value = 0.68
Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 30 | 2 |
subtype1 | 12 | 2 |
subtype2 | 11 | 0 |
subtype3 | 7 | 0 |
Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D2V6.png)
P value = 0.669 (Fisher's exact test), Q value = 0.8
Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 18 | 14 |
subtype1 | 9 | 5 |
subtype2 | 5 | 6 |
subtype3 | 4 | 3 |
Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D2V7.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 18 | 1 |
subtype1 | 7 | 1 |
subtype2 | 7 | 0 |
subtype3 | 4 | 0 |
Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D2V8.png)
P value = 0.396 (Fisher's exact test), Q value = 0.64
Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA |
---|---|---|---|
ALL | 1 | 30 | 1 |
subtype1 | 1 | 13 | 0 |
subtype2 | 0 | 11 | 0 |
subtype3 | 0 | 6 | 1 |
Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D2V10.png)
P value = 0.362 (Kruskal-Wallis (anova)), Q value = 0.6
Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 20 | 41.1 (15.0) |
subtype1 | 8 | 35.4 (12.2) |
subtype2 | 9 | 46.7 (16.1) |
subtype3 | 3 | 40.0 (17.3) |
Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D2V11.png)
P value = 0.676 (Kruskal-Wallis (anova)), Q value = 0.81
Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 1968.5 (11.4) |
subtype1 | 7 | 1969.9 (13.0) |
subtype2 | 6 | 1970.5 (12.9) |
subtype3 | 6 | 1965.0 (8.8) |
Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D2V12.png)
P value = 0.458 (Fisher's exact test), Q value = 0.67
Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R2 | RX |
---|---|---|---|
ALL | 26 | 1 | 2 |
subtype1 | 10 | 0 | 2 |
subtype2 | 10 | 1 | 0 |
subtype3 | 6 | 0 | 0 |
Figure S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D2V13.png)
P value = 0.00957 (Fisher's exact test), Q value = 0.045
Table S29. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 2 | 1 | 26 |
subtype1 | 0 | 0 | 13 |
subtype2 | 0 | 0 | 9 |
subtype3 | 2 | 1 | 4 |
Figure S27. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'
![](D2V14.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S30. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 1 | 28 |
subtype1 | 1 | 12 |
subtype2 | 0 | 9 |
subtype3 | 0 | 7 |
Figure S28. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D2V15.png)
Table S31. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 69 | 228 | 78 | 140 |
P value = 0.0133 (logrank test), Q value = 0.06
Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 492 | 167 | 0.0 - 238.3 (20.0) |
subtype1 | 65 | 20 | 0.1 - 129.5 (18.6) |
subtype2 | 218 | 60 | 0.1 - 238.3 (20.6) |
subtype3 | 75 | 32 | 0.0 - 86.1 (18.7) |
subtype4 | 134 | 55 | 0.1 - 232.2 (21.4) |
Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.00156 (Kruskal-Wallis (anova)), Q value = 0.013
Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 484 | 65.2 (10.0) |
subtype1 | 65 | 64.4 (11.0) |
subtype2 | 213 | 67.2 (8.9) |
subtype3 | 74 | 64.9 (10.8) |
subtype4 | 132 | 62.7 (10.2) |
Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D3V2.png)
P value = 0.12 (Fisher's exact test), Q value = 0.29
Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 135 | 139 | 1 | 52 | 72 | 72 | 11 | 26 |
subtype1 | 0 | 22 | 16 | 0 | 4 | 7 | 13 | 2 | 5 |
subtype2 | 2 | 74 | 63 | 1 | 20 | 28 | 27 | 3 | 8 |
subtype3 | 1 | 15 | 18 | 0 | 8 | 16 | 11 | 2 | 7 |
subtype4 | 2 | 24 | 42 | 0 | 20 | 21 | 21 | 4 | 6 |
Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D3V3.png)
P value = 0.115 (Fisher's exact test), Q value = 0.29
Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 170 | 278 | 46 | 19 |
subtype1 | 24 | 36 | 5 | 4 |
subtype2 | 90 | 112 | 16 | 9 |
subtype3 | 19 | 45 | 11 | 2 |
subtype4 | 37 | 85 | 14 | 4 |
Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D3V4.png)
P value = 0.249 (Fisher's exact test), Q value = 0.48
Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 332 | 96 | 76 |
subtype1 | 48 | 8 | 12 |
subtype2 | 154 | 40 | 27 |
subtype3 | 49 | 16 | 12 |
subtype4 | 81 | 32 | 25 |
Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D3V5.png)
P value = 0.187 (Fisher's exact test), Q value = 0.4
Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 349 | 25 |
subtype1 | 52 | 5 |
subtype2 | 156 | 7 |
subtype3 | 51 | 7 |
subtype4 | 90 | 6 |
Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D3V6.png)
P value = 0.0189 (Fisher's exact test), Q value = 0.078
Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 275 | 240 |
subtype1 | 28 | 41 |
subtype2 | 128 | 100 |
subtype3 | 35 | 43 |
subtype4 | 84 | 56 |
Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D3V7.png)
P value = 0.592 (Fisher's exact test), Q value = 0.75
Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 360 | 54 |
subtype1 | 52 | 9 |
subtype2 | 163 | 20 |
subtype3 | 49 | 10 |
subtype4 | 96 | 15 |
Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D3V8.png)
P value = 0.359 (Kruskal-Wallis (anova)), Q value = 0.6
Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 132 | 78.8 (28.6) |
subtype1 | 17 | 74.7 (36.6) |
subtype2 | 50 | 85.0 (22.2) |
subtype3 | 24 | 76.2 (28.6) |
subtype4 | 41 | 74.4 (31.4) |
Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D3V9.png)
P value = 0.382 (Fisher's exact test), Q value = 0.62
Table S41. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 106 | 320 | 5 | 19 | 2 | 3 | 2 | 23 | 1 | 5 | 10 |
subtype1 | 6 | 14 | 41 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 0 | 0 |
subtype2 | 7 | 50 | 132 | 3 | 11 | 1 | 1 | 2 | 10 | 1 | 3 | 7 |
subtype3 | 2 | 14 | 54 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 1 | 3 |
subtype4 | 4 | 28 | 93 | 2 | 6 | 0 | 2 | 0 | 4 | 0 | 1 | 0 |
Figure S38. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D3V10.png)
P value = 0.0303 (Kruskal-Wallis (anova)), Q value = 0.11
Table S42. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 355 | 41.6 (27.3) |
subtype1 | 54 | 40.7 (28.1) |
subtype2 | 152 | 38.2 (26.3) |
subtype3 | 55 | 41.9 (25.5) |
subtype4 | 94 | 47.5 (28.8) |
Figure S39. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D3V11.png)
P value = 0.173 (Kruskal-Wallis (anova)), Q value = 0.39
Table S43. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 276 | 1965.0 (12.6) |
subtype1 | 43 | 1965.9 (13.6) |
subtype2 | 119 | 1963.5 (12.1) |
subtype3 | 41 | 1968.3 (12.6) |
subtype4 | 73 | 1965.1 (12.5) |
Figure S40. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D3V12.png)
P value = 0.878 (Fisher's exact test), Q value = 0.96
Table S44. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 343 | 12 | 4 | 26 |
subtype1 | 43 | 2 | 0 | 5 |
subtype2 | 146 | 3 | 2 | 12 |
subtype3 | 55 | 2 | 1 | 3 |
subtype4 | 99 | 5 | 1 | 6 |
Figure S41. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D3V13.png)
P value = 0.561 (Fisher's exact test), Q value = 0.72
Table S45. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 7 | 51 | 389 |
subtype1 | 0 | 2 | 5 | 53 |
subtype2 | 0 | 2 | 20 | 180 |
subtype3 | 0 | 1 | 10 | 55 |
subtype4 | 1 | 2 | 16 | 101 |
Figure S42. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'
![](D3V14.png)
P value = 0.221 (Fisher's exact test), Q value = 0.44
Table S46. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 382 |
subtype1 | 1 | 51 |
subtype2 | 2 | 177 |
subtype3 | 3 | 55 |
subtype4 | 1 | 99 |
Figure S43. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D3V15.png)
Table S47. Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 145 | 148 | 164 |
P value = 0.131 (logrank test), Q value = 0.31
Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 435 | 145 | 0.1 - 238.3 (19.5) |
subtype1 | 137 | 35 | 0.1 - 238.3 (20.6) |
subtype2 | 141 | 58 | 0.1 - 232.2 (19.0) |
subtype3 | 157 | 52 | 0.1 - 163.1 (18.1) |
Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.206 (Kruskal-Wallis (anova)), Q value = 0.42
Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 428 | 65.0 (10.2) |
subtype1 | 134 | 66.0 (9.7) |
subtype2 | 140 | 63.9 (10.4) |
subtype3 | 154 | 65.2 (10.3) |
Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D4V2.png)
P value = 0.00037 (Fisher's exact test), Q value = 0.0061
Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 120 | 124 | 1 | 49 | 62 | 64 | 9 | 21 |
subtype1 | 1 | 57 | 34 | 1 | 16 | 17 | 12 | 1 | 5 |
subtype2 | 4 | 35 | 37 | 0 | 18 | 14 | 26 | 4 | 10 |
subtype3 | 0 | 28 | 53 | 0 | 15 | 31 | 26 | 4 | 6 |
Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D4V3.png)
P value = 0.00102 (Fisher's exact test), Q value = 0.011
Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 154 | 243 | 41 | 16 |
subtype1 | 68 | 62 | 10 | 3 |
subtype2 | 48 | 83 | 11 | 5 |
subtype3 | 38 | 98 | 20 | 8 |
Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D4V4.png)
P value = 0.0623 (Fisher's exact test), Q value = 0.18
Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 298 | 83 | 66 |
subtype1 | 101 | 25 | 12 |
subtype2 | 92 | 24 | 30 |
subtype3 | 105 | 34 | 24 |
Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D4V5.png)
P value = 0.26 (Fisher's exact test), Q value = 0.49
Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 295 | 19 |
subtype1 | 84 | 4 |
subtype2 | 98 | 10 |
subtype3 | 113 | 5 |
Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D4V6.png)
P value = 2e-05 (Fisher's exact test), Q value = 6e-04
Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 243 | 214 |
subtype1 | 97 | 48 |
subtype2 | 59 | 89 |
subtype3 | 87 | 77 |
Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
![](D4V7.png)
P value = 0.0322 (Fisher's exact test), Q value = 0.12
Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 328 | 49 |
subtype1 | 106 | 16 |
subtype2 | 100 | 23 |
subtype3 | 122 | 10 |
Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D4V8.png)
P value = 0.402 (Kruskal-Wallis (anova)), Q value = 0.64
Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 118 | 79.8 (28.3) |
subtype1 | 32 | 84.7 (25.3) |
subtype2 | 46 | 76.1 (31.3) |
subtype3 | 40 | 80.2 (27.1) |
Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D4V9.png)
P value = 0.14 (Fisher's exact test), Q value = 0.33
Table S57. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 91 | 280 | 5 | 19 | 1 | 2 | 2 | 22 | 1 | 5 | 10 |
subtype1 | 5 | 23 | 88 | 4 | 8 | 0 | 1 | 2 | 7 | 1 | 0 | 6 |
subtype2 | 8 | 29 | 92 | 0 | 3 | 1 | 0 | 0 | 9 | 0 | 3 | 3 |
subtype3 | 6 | 39 | 100 | 1 | 8 | 0 | 1 | 0 | 6 | 0 | 2 | 1 |
Figure S53. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D4V10.png)
P value = 0.00402 (Kruskal-Wallis (anova)), Q value = 0.023
Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 313 | 40.7 (27.3) |
subtype1 | 91 | 37.4 (24.6) |
subtype2 | 110 | 47.4 (29.6) |
subtype3 | 112 | 36.8 (25.9) |
Figure S54. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D4V11.png)
P value = 0.888 (Kruskal-Wallis (anova)), Q value = 0.96
Table S59. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 251 | 1965.3 (12.5) |
subtype1 | 72 | 1964.7 (11.4) |
subtype2 | 88 | 1965.8 (12.9) |
subtype3 | 91 | 1965.2 (13.0) |
Figure S55. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D4V12.png)
P value = 0.8 (Fisher's exact test), Q value = 0.91
Table S60. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 293 | 10 | 1 | 23 |
subtype1 | 87 | 2 | 1 | 9 |
subtype2 | 94 | 4 | 0 | 6 |
subtype3 | 112 | 4 | 0 | 8 |
Figure S56. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D4V13.png)
P value = 0.183 (Fisher's exact test), Q value = 0.4
Table S61. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 6 | 49 | 352 |
subtype1 | 0 | 18 | 114 |
subtype2 | 2 | 19 | 114 |
subtype3 | 4 | 12 | 124 |
Figure S57. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'
![](D4V14.png)
P value = 0.0463 (Fisher's exact test), Q value = 0.15
Table S62. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 341 |
subtype1 | 2 | 118 |
subtype2 | 5 | 108 |
subtype3 | 0 | 115 |
Figure S58. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'
![](D4V15.png)
Table S63. Description of clustering approach #5: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 91 | 23 | 67 | 75 | 109 |
P value = 0.997 (logrank test), Q value = 1
Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 347 | 133 | 0.1 - 238.3 (21.5) |
subtype1 | 85 | 31 | 0.1 - 123.6 (22.8) |
subtype2 | 22 | 7 | 0.1 - 73.9 (18.8) |
subtype3 | 65 | 32 | 0.3 - 163.1 (23.1) |
subtype4 | 70 | 27 | 0.8 - 97.7 (21.7) |
subtype5 | 105 | 36 | 0.1 - 238.3 (20.5) |
Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.00173 (Kruskal-Wallis (anova)), Q value = 0.013
Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 344 | 64.4 (9.8) |
subtype1 | 83 | 63.7 (10.3) |
subtype2 | 23 | 65.3 (9.6) |
subtype3 | 66 | 64.9 (9.3) |
subtype4 | 68 | 60.6 (9.4) |
subtype5 | 104 | 66.9 (9.4) |
Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D5V2.png)
P value = 0.333 (Fisher's exact test), Q value = 0.58
Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 2 | 96 | 95 | 1 | 38 | 50 | 57 | 8 | 18 |
subtype1 | 1 | 31 | 23 | 0 | 10 | 13 | 11 | 1 | 1 |
subtype2 | 0 | 8 | 6 | 0 | 1 | 1 | 4 | 0 | 3 |
subtype3 | 0 | 11 | 20 | 0 | 9 | 9 | 9 | 3 | 6 |
subtype4 | 0 | 15 | 20 | 1 | 5 | 14 | 13 | 3 | 4 |
subtype5 | 1 | 31 | 26 | 0 | 13 | 13 | 20 | 1 | 4 |
Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D5V3.png)
P value = 0.205 (Fisher's exact test), Q value = 0.42
Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 117 | 203 | 28 | 16 |
subtype1 | 35 | 50 | 5 | 1 |
subtype2 | 8 | 12 | 1 | 1 |
subtype3 | 15 | 39 | 6 | 7 |
subtype4 | 19 | 46 | 6 | 4 |
subtype5 | 40 | 56 | 10 | 3 |
Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D5V4.png)
P value = 0.434 (Fisher's exact test), Q value = 0.67
Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 225 | 72 | 59 |
subtype1 | 54 | 20 | 13 |
subtype2 | 14 | 4 | 3 |
subtype3 | 44 | 14 | 8 |
subtype4 | 41 | 20 | 14 |
subtype5 | 72 | 14 | 21 |
Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D5V5.png)
P value = 0.0484 (Fisher's exact test), Q value = 0.15
Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 259 | 17 |
subtype1 | 67 | 1 |
subtype2 | 15 | 3 |
subtype3 | 51 | 6 |
subtype4 | 50 | 4 |
subtype5 | 76 | 3 |
Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D5V6.png)
P value = 0.157 (Fisher's exact test), Q value = 0.36
Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 196 | 169 |
subtype1 | 57 | 34 |
subtype2 | 15 | 8 |
subtype3 | 32 | 35 |
subtype4 | 35 | 40 |
subtype5 | 57 | 52 |
Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D5V7.png)
P value = 0.202 (Fisher's exact test), Q value = 0.42
Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 263 | 41 |
subtype1 | 65 | 11 |
subtype2 | 15 | 3 |
subtype3 | 54 | 4 |
subtype4 | 49 | 13 |
subtype5 | 80 | 10 |
Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D5V8.png)
P value = 0.306 (Kruskal-Wallis (anova)), Q value = 0.55
Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 97 | 77.0 (30.5) |
subtype1 | 24 | 69.2 (38.7) |
subtype2 | 3 | 90.0 (10.0) |
subtype3 | 23 | 87.0 (18.4) |
subtype4 | 24 | 78.8 (30.0) |
subtype5 | 23 | 71.7 (31.3) |
Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D5V9.png)
P value = 0.00724 (Fisher's exact test), Q value = 0.037
Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 13 | 73 | 233 | 5 | 13 | 1 | 2 | 2 | 13 | 1 | 4 | 5 |
subtype1 | 2 | 12 | 67 | 1 | 3 | 0 | 0 | 1 | 4 | 0 | 0 | 1 |
subtype2 | 2 | 8 | 10 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
subtype3 | 2 | 21 | 35 | 0 | 5 | 0 | 1 | 0 | 2 | 0 | 0 | 1 |
subtype4 | 3 | 17 | 50 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 0 |
subtype5 | 4 | 15 | 71 | 4 | 4 | 0 | 1 | 1 | 5 | 1 | 0 | 3 |
Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D5V10.png)
P value = 0.78 (Kruskal-Wallis (anova)), Q value = 0.89
Table S74. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 259 | 41.6 (27.9) |
subtype1 | 47 | 41.8 (24.1) |
subtype2 | 19 | 41.3 (32.5) |
subtype3 | 54 | 45.5 (31.0) |
subtype4 | 61 | 41.9 (29.1) |
subtype5 | 78 | 38.7 (25.9) |
Figure S69. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D5V11.png)
P value = 0.000782 (Kruskal-Wallis (anova)), Q value = 0.01
Table S75. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 205 | 1964.9 (13.0) |
subtype1 | 33 | 1965.3 (13.2) |
subtype2 | 14 | 1961.7 (10.8) |
subtype3 | 45 | 1959.4 (11.5) |
subtype4 | 48 | 1971.1 (13.5) |
subtype5 | 65 | 1964.6 (12.3) |
Figure S70. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D5V12.png)
P value = 0.00716 (Fisher's exact test), Q value = 0.037
Table S76. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 235 | 9 | 3 | 21 |
subtype1 | 59 | 2 | 0 | 5 |
subtype2 | 12 | 0 | 3 | 5 |
subtype3 | 50 | 3 | 0 | 4 |
subtype4 | 50 | 1 | 0 | 3 |
subtype5 | 64 | 3 | 0 | 4 |
Figure S71. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D5V13.png)
P value = 0.332 (Fisher's exact test), Q value = 0.58
Table S77. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 5 | 39 | 274 |
subtype1 | 1 | 6 | 73 |
subtype2 | 1 | 3 | 13 |
subtype3 | 1 | 6 | 52 |
subtype4 | 1 | 12 | 50 |
subtype5 | 1 | 12 | 86 |
Figure S72. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'
![](D5V14.png)
P value = 0.55 (Fisher's exact test), Q value = 0.71
Table S78. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 6 | 260 |
subtype1 | 1 | 68 |
subtype2 | 0 | 15 |
subtype3 | 0 | 42 |
subtype4 | 3 | 51 |
subtype5 | 2 | 84 |
Figure S73. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D5V15.png)
Table S79. Description of clustering approach #6: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 167 | 135 | 63 |
P value = 0.29 (logrank test), Q value = 0.52
Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 347 | 133 | 0.1 - 238.3 (21.5) |
subtype1 | 159 | 64 | 0.1 - 164.1 (22.7) |
subtype2 | 127 | 51 | 0.4 - 97.7 (20.1) |
subtype3 | 61 | 18 | 0.1 - 238.3 (21.6) |
Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.092 (Kruskal-Wallis (anova)), Q value = 0.25
Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 344 | 64.4 (9.8) |
subtype1 | 160 | 65.2 (9.3) |
subtype2 | 124 | 62.7 (10.2) |
subtype3 | 60 | 65.8 (10.2) |
Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D6V2.png)
P value = 0.663 (Fisher's exact test), Q value = 0.8
Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 2 | 96 | 95 | 1 | 38 | 50 | 57 | 8 | 18 |
subtype1 | 1 | 51 | 40 | 0 | 16 | 19 | 27 | 4 | 9 |
subtype2 | 1 | 28 | 38 | 1 | 13 | 26 | 20 | 3 | 5 |
subtype3 | 0 | 17 | 17 | 0 | 9 | 5 | 10 | 1 | 4 |
Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D6V3.png)
P value = 0.105 (Fisher's exact test), Q value = 0.27
Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 117 | 203 | 28 | 16 |
subtype1 | 61 | 86 | 9 | 10 |
subtype2 | 34 | 83 | 15 | 3 |
subtype3 | 22 | 34 | 4 | 3 |
Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D6V4.png)
P value = 0.483 (Fisher's exact test), Q value = 0.68
Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 225 | 72 | 59 |
subtype1 | 102 | 31 | 27 |
subtype2 | 81 | 33 | 21 |
subtype3 | 42 | 8 | 11 |
Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D6V5.png)
P value = 0.791 (Fisher's exact test), Q value = 0.9
Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 259 | 17 |
subtype1 | 121 | 9 |
subtype2 | 97 | 5 |
subtype3 | 41 | 3 |
Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D6V6.png)
P value = 0.539 (Fisher's exact test), Q value = 0.71
Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 196 | 169 |
subtype1 | 91 | 76 |
subtype2 | 68 | 67 |
subtype3 | 37 | 26 |
Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D6V7.png)
P value = 0.105 (Fisher's exact test), Q value = 0.27
Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 263 | 41 |
subtype1 | 125 | 15 |
subtype2 | 89 | 21 |
subtype3 | 49 | 5 |
Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D6V8.png)
P value = 0.824 (Kruskal-Wallis (anova)), Q value = 0.92
Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 97 | 77.0 (30.5) |
subtype1 | 42 | 77.4 (30.4) |
subtype2 | 39 | 77.7 (31.3) |
subtype3 | 16 | 74.4 (30.5) |
Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D6V9.png)
P value = 0.00138 (Fisher's exact test), Q value = 0.013
Table S89. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 13 | 73 | 233 | 5 | 13 | 1 | 2 | 2 | 13 | 1 | 4 | 5 |
subtype1 | 6 | 41 | 94 | 2 | 8 | 1 | 2 | 1 | 8 | 1 | 0 | 3 |
subtype2 | 3 | 21 | 103 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 1 |
subtype3 | 4 | 11 | 36 | 3 | 4 | 0 | 0 | 0 | 4 | 0 | 0 | 1 |
Figure S83. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D6V10.png)
P value = 0.982 (Kruskal-Wallis (anova)), Q value = 1
Table S90. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 259 | 41.6 (27.9) |
subtype1 | 112 | 42.4 (30.0) |
subtype2 | 95 | 42.1 (28.1) |
subtype3 | 52 | 39.0 (22.4) |
Figure S84. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D6V11.png)
P value = 0.00327 (Kruskal-Wallis (anova)), Q value = 0.02
Table S91. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 205 | 1964.9 (13.0) |
subtype1 | 86 | 1961.1 (12.2) |
subtype2 | 74 | 1968.4 (13.5) |
subtype3 | 45 | 1966.3 (12.2) |
Figure S85. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D6V12.png)
P value = 0.776 (Fisher's exact test), Q value = 0.89
Table S92. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 235 | 9 | 3 | 21 |
subtype1 | 110 | 6 | 2 | 13 |
subtype2 | 88 | 2 | 1 | 5 |
subtype3 | 37 | 1 | 0 | 3 |
Figure S86. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D6V13.png)
P value = 0.363 (Fisher's exact test), Q value = 0.6
Table S93. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 5 | 39 | 274 |
subtype1 | 2 | 14 | 126 |
subtype2 | 3 | 19 | 94 |
subtype3 | 0 | 6 | 54 |
Figure S87. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'
![](D6V14.png)
P value = 0.0458 (Fisher's exact test), Q value = 0.15
Table S94. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 6 | 260 |
subtype1 | 0 | 117 |
subtype2 | 4 | 95 |
subtype3 | 2 | 48 |
Figure S88. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D6V15.png)
Table S95. Description of clustering approach #7: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 167 | 197 | 150 |
P value = 0.00175 (logrank test), Q value = 0.013
Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 491 | 167 | 0.0 - 238.3 (19.9) |
subtype1 | 158 | 37 | 0.1 - 238.3 (20.4) |
subtype2 | 189 | 81 | 0.0 - 221.3 (19.1) |
subtype3 | 144 | 49 | 0.1 - 232.2 (19.7) |
Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.0479 (Kruskal-Wallis (anova)), Q value = 0.15
Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 65.3 (10.0) |
subtype1 | 157 | 66.9 (9.3) |
subtype2 | 184 | 64.2 (10.6) |
subtype3 | 142 | 64.9 (9.6) |
Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D7V2.png)
P value = 0.00325 (Fisher's exact test), Q value = 0.02
Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 133 | 139 | 1 | 52 | 71 | 73 | 11 | 27 |
subtype1 | 2 | 63 | 46 | 0 | 14 | 15 | 19 | 1 | 5 |
subtype2 | 3 | 33 | 54 | 1 | 21 | 35 | 29 | 7 | 14 |
subtype3 | 0 | 37 | 39 | 0 | 17 | 21 | 25 | 3 | 8 |
Figure S91. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D7V3.png)
P value = 0.00156 (Fisher's exact test), Q value = 0.013
Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 168 | 277 | 47 | 19 |
subtype1 | 77 | 72 | 13 | 4 |
subtype2 | 49 | 117 | 19 | 10 |
subtype3 | 42 | 88 | 15 | 5 |
Figure S92. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D7V4.png)
P value = 0.0266 (Fisher's exact test), Q value = 0.1
Table S100. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 330 | 96 | 76 |
subtype1 | 119 | 25 | 17 |
subtype2 | 113 | 47 | 33 |
subtype3 | 98 | 24 | 26 |
Figure S93. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D7V5.png)
P value = 0.445 (Fisher's exact test), Q value = 0.67
Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 346 | 25 |
subtype1 | 109 | 5 |
subtype2 | 129 | 12 |
subtype3 | 108 | 8 |
Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D7V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 0.00045
Table S102. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 276 | 238 |
subtype1 | 112 | 55 |
subtype2 | 113 | 84 |
subtype3 | 51 | 99 |
Figure S95. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D7V7.png)
P value = 0.0268 (Fisher's exact test), Q value = 0.1
Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 356 | 55 |
subtype1 | 127 | 10 |
subtype2 | 129 | 27 |
subtype3 | 100 | 18 |
Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D7V8.png)
P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.4
Table S104. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 130 | 78.3 (28.9) |
subtype1 | 38 | 83.7 (26.3) |
subtype2 | 58 | 74.7 (30.8) |
subtype3 | 34 | 78.5 (28.2) |
Figure S97. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D7V9.png)
P value = 1e-05 (Fisher's exact test), Q value = 0.00045
Table S105. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 106 | 319 | 5 | 19 | 2 | 3 | 2 | 23 | 1 | 5 | 10 |
subtype1 | 7 | 35 | 90 | 5 | 14 | 0 | 1 | 1 | 8 | 1 | 1 | 4 |
subtype2 | 3 | 33 | 147 | 0 | 3 | 2 | 2 | 0 | 4 | 0 | 3 | 0 |
subtype3 | 9 | 38 | 82 | 0 | 2 | 0 | 0 | 1 | 11 | 0 | 1 | 6 |
Figure S98. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D7V10.png)
P value = 0.0931 (Kruskal-Wallis (anova)), Q value = 0.25
Table S106. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 351 | 41.6 (27.3) |
subtype1 | 97 | 37.8 (25.7) |
subtype2 | 132 | 44.9 (27.7) |
subtype3 | 122 | 41.0 (27.9) |
Figure S99. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D7V11.png)
P value = 0.355 (Kruskal-Wallis (anova)), Q value = 0.6
Table S107. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 273 | 1965.0 (12.5) |
subtype1 | 80 | 1964.1 (10.3) |
subtype2 | 98 | 1966.3 (13.9) |
subtype3 | 95 | 1964.3 (12.7) |
Figure S100. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D7V12.png)
P value = 0.645 (Fisher's exact test), Q value = 0.8
Table S108. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 342 | 12 | 4 | 25 |
subtype1 | 112 | 3 | 1 | 11 |
subtype2 | 127 | 6 | 3 | 8 |
subtype3 | 103 | 3 | 0 | 6 |
Figure S101. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D7V13.png)
P value = 0.972 (Fisher's exact test), Q value = 1
Table S109. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 51 | 387 |
subtype1 | 0 | 3 | 18 | 131 |
subtype2 | 1 | 2 | 19 | 148 |
subtype3 | 0 | 3 | 14 | 108 |
Figure S102. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'
![](D7V14.png)
P value = 0.496 (Fisher's exact test), Q value = 0.68
Table S110. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 381 |
subtype1 | 1 | 136 |
subtype2 | 4 | 147 |
subtype3 | 2 | 98 |
Figure S103. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D7V15.png)
Table S111. Description of clustering approach #8: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Number of samples | 79 | 112 | 97 | 48 | 52 | 49 | 77 |
P value = 2.96e-08 (logrank test), Q value = 5.3e-06
Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 491 | 167 | 0.0 - 238.3 (19.9) |
subtype1 | 75 | 22 | 0.1 - 163.1 (19.8) |
subtype2 | 107 | 30 | 0.4 - 97.7 (19.1) |
subtype3 | 93 | 38 | 0.1 - 238.3 (20.0) |
subtype4 | 45 | 16 | 0.4 - 129.5 (26.5) |
subtype5 | 49 | 13 | 0.1 - 88.0 (16.0) |
subtype6 | 48 | 31 | 0.0 - 55.9 (15.1) |
subtype7 | 74 | 17 | 0.1 - 164.1 (22.7) |
Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.000674 (Kruskal-Wallis (anova)), Q value = 0.0093
Table S113. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 483 | 65.3 (10.0) |
subtype1 | 75 | 68.3 (9.2) |
subtype2 | 103 | 63.5 (10.8) |
subtype3 | 90 | 65.5 (9.7) |
subtype4 | 46 | 67.7 (7.7) |
subtype5 | 49 | 60.7 (9.6) |
subtype6 | 47 | 66.0 (9.9) |
subtype7 | 73 | 65.5 (10.2) |
Figure S105. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D8V2.png)
P value = 0.0028 (Fisher's exact test), Q value = 0.019
Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 133 | 139 | 1 | 52 | 71 | 73 | 11 | 27 |
subtype1 | 0 | 30 | 22 | 0 | 8 | 6 | 9 | 1 | 3 |
subtype2 | 1 | 16 | 35 | 0 | 9 | 24 | 18 | 3 | 6 |
subtype3 | 3 | 26 | 27 | 1 | 14 | 7 | 11 | 2 | 6 |
subtype4 | 0 | 16 | 16 | 0 | 4 | 5 | 3 | 2 | 2 |
subtype5 | 0 | 10 | 15 | 0 | 7 | 5 | 12 | 1 | 2 |
subtype6 | 0 | 7 | 7 | 0 | 3 | 12 | 12 | 2 | 6 |
subtype7 | 1 | 28 | 17 | 0 | 7 | 12 | 8 | 0 | 2 |
Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D8V3.png)
P value = 0.00114 (Fisher's exact test), Q value = 0.012
Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 168 | 277 | 47 | 19 |
subtype1 | 36 | 36 | 4 | 3 |
subtype2 | 24 | 69 | 13 | 5 |
subtype3 | 39 | 51 | 3 | 3 |
subtype4 | 17 | 24 | 4 | 3 |
subtype5 | 11 | 35 | 5 | 1 |
subtype6 | 8 | 29 | 8 | 3 |
subtype7 | 33 | 33 | 10 | 1 |
Figure S107. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D8V4.png)
P value = 0.00307 (Fisher's exact test), Q value = 0.02
Table S116. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 330 | 96 | 76 |
subtype1 | 57 | 12 | 9 |
subtype2 | 66 | 25 | 19 |
subtype3 | 60 | 23 | 12 |
subtype4 | 37 | 8 | 2 |
subtype5 | 36 | 3 | 12 |
subtype6 | 22 | 12 | 14 |
subtype7 | 52 | 13 | 8 |
Figure S108. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D8V5.png)
P value = 0.507 (Fisher's exact test), Q value = 0.68
Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 346 | 25 |
subtype1 | 52 | 3 |
subtype2 | 79 | 4 |
subtype3 | 55 | 6 |
subtype4 | 38 | 2 |
subtype5 | 38 | 2 |
subtype6 | 37 | 6 |
subtype7 | 47 | 2 |
Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D8V6.png)
P value = 2e-05 (Fisher's exact test), Q value = 6e-04
Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 276 | 238 |
subtype1 | 63 | 16 |
subtype2 | 59 | 53 |
subtype3 | 52 | 45 |
subtype4 | 16 | 32 |
subtype5 | 23 | 29 |
subtype6 | 19 | 30 |
subtype7 | 44 | 33 |
Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D8V7.png)
P value = 0.0234 (Fisher's exact test), Q value = 0.094
Table S119. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 356 | 55 |
subtype1 | 62 | 2 |
subtype2 | 76 | 10 |
subtype3 | 64 | 15 |
subtype4 | 37 | 3 |
subtype5 | 36 | 5 |
subtype6 | 31 | 9 |
subtype7 | 50 | 11 |
Figure S111. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D8V8.png)
P value = 0.384 (Kruskal-Wallis (anova)), Q value = 0.62
Table S120. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 130 | 78.3 (28.9) |
subtype1 | 15 | 87.3 (25.2) |
subtype2 | 28 | 77.5 (28.5) |
subtype3 | 32 | 79.1 (29.1) |
subtype4 | 11 | 89.1 (13.0) |
subtype5 | 14 | 66.4 (36.7) |
subtype6 | 10 | 72.0 (33.3) |
subtype7 | 20 | 77.0 (29.6) |
Figure S112. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D8V9.png)
P value = 0.00013 (Fisher's exact test), Q value = 0.0026
Table S121. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 106 | 319 | 5 | 19 | 2 | 3 | 2 | 23 | 1 | 5 | 10 |
subtype1 | 5 | 16 | 45 | 1 | 6 | 0 | 1 | 0 | 5 | 0 | 0 | 0 |
subtype2 | 2 | 18 | 81 | 0 | 3 | 1 | 2 | 0 | 4 | 0 | 1 | 0 |
subtype3 | 3 | 15 | 71 | 1 | 2 | 1 | 0 | 0 | 2 | 0 | 2 | 0 |
subtype4 | 2 | 19 | 18 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 0 | 1 |
subtype5 | 4 | 10 | 36 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
subtype6 | 1 | 12 | 31 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 |
subtype7 | 2 | 16 | 37 | 3 | 6 | 0 | 0 | 2 | 3 | 1 | 1 | 6 |
Figure S113. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D8V10.png)
P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.14
Table S122. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 351 | 41.6 (27.3) |
subtype1 | 46 | 35.4 (25.7) |
subtype2 | 78 | 41.1 (25.7) |
subtype3 | 64 | 49.9 (31.5) |
subtype4 | 37 | 40.4 (30.5) |
subtype5 | 42 | 37.3 (26.3) |
subtype6 | 39 | 45.9 (24.1) |
subtype7 | 45 | 38.2 (24.2) |
Figure S114. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D8V11.png)
P value = 0.000498 (Kruskal-Wallis (anova)), Q value = 0.0075
Table S123. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 273 | 1965.0 (12.5) |
subtype1 | 37 | 1963.0 (10.3) |
subtype2 | 59 | 1967.5 (14.2) |
subtype3 | 50 | 1963.5 (12.3) |
subtype4 | 29 | 1956.9 (11.4) |
subtype5 | 34 | 1970.3 (9.7) |
subtype6 | 29 | 1965.1 (13.3) |
subtype7 | 35 | 1966.2 (11.2) |
Figure S115. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D8V12.png)
P value = 0.244 (Fisher's exact test), Q value = 0.47
Table S124. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 342 | 12 | 4 | 25 |
subtype1 | 54 | 4 | 0 | 2 |
subtype2 | 77 | 1 | 2 | 3 |
subtype3 | 58 | 3 | 2 | 9 |
subtype4 | 28 | 0 | 0 | 3 |
subtype5 | 37 | 1 | 0 | 2 |
subtype6 | 35 | 3 | 0 | 2 |
subtype7 | 53 | 0 | 0 | 4 |
Figure S116. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D8V13.png)
P value = 0.601 (Fisher's exact test), Q value = 0.76
Table S125. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 8 | 51 | 387 |
subtype1 | 0 | 2 | 6 | 62 |
subtype2 | 1 | 3 | 9 | 84 |
subtype3 | 0 | 1 | 14 | 70 |
subtype4 | 0 | 0 | 5 | 37 |
subtype5 | 0 | 0 | 5 | 38 |
subtype6 | 0 | 2 | 2 | 38 |
subtype7 | 0 | 0 | 10 | 58 |
Figure S117. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'
![](D8V14.png)
P value = 0.0361 (Fisher's exact test), Q value = 0.13
Table S126. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 381 |
subtype1 | 1 | 60 |
subtype2 | 0 | 85 |
subtype3 | 4 | 71 |
subtype4 | 0 | 34 |
subtype5 | 0 | 36 |
subtype6 | 2 | 31 |
subtype7 | 0 | 64 |
Figure S118. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D8V15.png)
Table S127. Description of clustering approach #9: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 190 | 227 | 95 |
P value = 0.0533 (logrank test), Q value = 0.16
Table S128. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 489 | 166 | 0.0 - 238.3 (20.0) |
subtype1 | 181 | 53 | 0.1 - 164.1 (19.8) |
subtype2 | 219 | 74 | 0.0 - 232.2 (20.2) |
subtype3 | 89 | 39 | 0.1 - 238.3 (19.7) |
Figure S119. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.0102 (Kruskal-Wallis (anova)), Q value = 0.047
Table S129. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 482 | 65.3 (10.0) |
subtype1 | 180 | 67.0 (9.4) |
subtype2 | 217 | 64.0 (10.3) |
subtype3 | 85 | 65.1 (9.7) |
Figure S120. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D9V2.png)
P value = 0.00182 (Fisher's exact test), Q value = 0.013
Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 134 | 139 | 1 | 52 | 70 | 73 | 11 | 25 |
subtype1 | 1 | 71 | 40 | 0 | 19 | 25 | 22 | 1 | 9 |
subtype2 | 4 | 47 | 69 | 0 | 22 | 34 | 30 | 8 | 13 |
subtype3 | 0 | 16 | 30 | 1 | 11 | 11 | 21 | 2 | 3 |
Figure S121. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D9V3.png)
P value = 0.0176 (Fisher's exact test), Q value = 0.074
Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 169 | 274 | 47 | 19 |
subtype1 | 83 | 85 | 16 | 6 |
subtype2 | 60 | 133 | 23 | 9 |
subtype3 | 26 | 56 | 8 | 4 |
Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D9V4.png)
P value = 0.276 (Fisher's exact test), Q value = 0.5
Table S132. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 329 | 95 | 76 |
subtype1 | 124 | 39 | 21 |
subtype2 | 149 | 38 | 36 |
subtype3 | 56 | 18 | 19 |
Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D9V5.png)
P value = 0.546 (Fisher's exact test), Q value = 0.71
Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 346 | 23 |
subtype1 | 130 | 8 |
subtype2 | 157 | 13 |
subtype3 | 59 | 2 |
Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D9V6.png)
P value = 0.476 (Fisher's exact test), Q value = 0.68
Table S134. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 273 | 239 |
subtype1 | 108 | 82 |
subtype2 | 116 | 111 |
subtype3 | 49 | 46 |
Figure S125. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
![](D9V7.png)
P value = 0.241 (Fisher's exact test), Q value = 0.47
Table S135. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 355 | 54 |
subtype1 | 140 | 16 |
subtype2 | 147 | 29 |
subtype3 | 68 | 9 |
Figure S126. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D9V8.png)
P value = 0.277 (Kruskal-Wallis (anova)), Q value = 0.5
Table S136. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 128 | 78.5 (29.1) |
subtype1 | 43 | 81.4 (27.8) |
subtype2 | 66 | 79.8 (28.0) |
subtype3 | 19 | 67.4 (34.1) |
Figure S127. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D9V9.png)
P value = 0.00096 (Fisher's exact test), Q value = 0.011
Table S137. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 105 | 319 | 5 | 19 | 2 | 2 | 2 | 23 | 1 | 5 | 10 |
subtype1 | 10 | 45 | 99 | 4 | 12 | 1 | 1 | 2 | 8 | 1 | 2 | 5 |
subtype2 | 8 | 52 | 148 | 0 | 4 | 1 | 0 | 0 | 9 | 0 | 3 | 2 |
subtype3 | 1 | 8 | 72 | 1 | 3 | 0 | 1 | 0 | 6 | 0 | 0 | 3 |
Figure S128. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D9V10.png)
P value = 0.0797 (Kruskal-Wallis (anova)), Q value = 0.22
Table S138. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 351 | 41.8 (27.4) |
subtype1 | 125 | 38.2 (25.8) |
subtype2 | 168 | 43.2 (27.9) |
subtype3 | 58 | 45.3 (28.8) |
Figure S129. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D9V11.png)
P value = 0.017 (Kruskal-Wallis (anova)), Q value = 0.073
Table S139. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 273 | 1964.8 (12.5) |
subtype1 | 104 | 1962.3 (11.9) |
subtype2 | 129 | 1967.1 (12.7) |
subtype3 | 40 | 1963.5 (12.1) |
Figure S130. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D9V12.png)
P value = 0.45 (Fisher's exact test), Q value = 0.67
Table S140. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 339 | 12 | 4 | 26 |
subtype1 | 122 | 2 | 2 | 10 |
subtype2 | 162 | 7 | 2 | 9 |
subtype3 | 55 | 3 | 0 | 7 |
Figure S131. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D9V13.png)
P value = 0.44 (Fisher's exact test), Q value = 0.67
Table S141. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 8 | 51 | 386 |
subtype1 | 1 | 18 | 152 |
subtype2 | 5 | 25 | 158 |
subtype3 | 2 | 8 | 76 |
Figure S132. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'
![](D9V14.png)
P value = 0.369 (Fisher's exact test), Q value = 0.61
Table S142. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 379 |
subtype1 | 1 | 144 |
subtype2 | 5 | 161 |
subtype3 | 1 | 74 |
Figure S133. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'
![](D9V15.png)
Table S143. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 72 | 198 | 142 | 100 |
P value = 0.818 (logrank test), Q value = 0.92
Table S144. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 489 | 166 | 0.0 - 238.3 (20.0) |
subtype1 | 69 | 22 | 0.1 - 129.5 (21.4) |
subtype2 | 191 | 66 | 0.0 - 238.3 (19.0) |
subtype3 | 134 | 44 | 0.1 - 164.1 (19.1) |
subtype4 | 95 | 34 | 0.1 - 232.2 (21.9) |
Figure S134. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.00155 (Kruskal-Wallis (anova)), Q value = 0.013
Table S145. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 482 | 65.3 (10.0) |
subtype1 | 68 | 68.1 (7.8) |
subtype2 | 188 | 63.7 (10.0) |
subtype3 | 133 | 66.9 (9.8) |
subtype4 | 93 | 64.1 (10.8) |
Figure S135. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D10V2.png)
P value = 0.659 (Fisher's exact test), Q value = 0.8
Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 134 | 139 | 1 | 52 | 70 | 73 | 11 | 25 |
subtype1 | 0 | 28 | 17 | 0 | 3 | 10 | 10 | 1 | 3 |
subtype2 | 3 | 40 | 60 | 0 | 20 | 30 | 30 | 6 | 9 |
subtype3 | 1 | 42 | 35 | 1 | 16 | 19 | 16 | 2 | 8 |
subtype4 | 1 | 24 | 27 | 0 | 13 | 11 | 17 | 2 | 5 |
Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D10V3.png)
P value = 0.133 (Fisher's exact test), Q value = 0.32
Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 169 | 274 | 47 | 19 |
subtype1 | 31 | 33 | 4 | 4 |
subtype2 | 50 | 120 | 19 | 8 |
subtype3 | 55 | 67 | 14 | 4 |
subtype4 | 33 | 54 | 10 | 3 |
Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D10V4.png)
P value = 0.716 (Fisher's exact test), Q value = 0.84
Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 329 | 95 | 76 |
subtype1 | 48 | 14 | 7 |
subtype2 | 128 | 36 | 32 |
subtype3 | 89 | 29 | 18 |
subtype4 | 64 | 16 | 19 |
Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D10V5.png)
P value = 0.856 (Fisher's exact test), Q value = 0.94
Table S149. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 346 | 23 |
subtype1 | 50 | 2 |
subtype2 | 145 | 9 |
subtype3 | 89 | 7 |
subtype4 | 62 | 5 |
Figure S139. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D10V6.png)
P value = 0.00477 (Fisher's exact test), Q value = 0.027
Table S150. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 273 | 239 |
subtype1 | 35 | 37 |
subtype2 | 113 | 85 |
subtype3 | 86 | 56 |
subtype4 | 39 | 61 |
Figure S140. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
![](D10V7.png)
P value = 0.69 (Fisher's exact test), Q value = 0.82
Table S151. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 355 | 54 |
subtype1 | 54 | 9 |
subtype2 | 132 | 23 |
subtype3 | 99 | 11 |
subtype4 | 70 | 11 |
Figure S141. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D10V8.png)
P value = 0.651 (Kruskal-Wallis (anova)), Q value = 0.8
Table S152. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 128 | 78.5 (29.1) |
subtype1 | 14 | 80.0 (25.7) |
subtype2 | 54 | 78.7 (31.0) |
subtype3 | 30 | 81.0 (28.4) |
subtype4 | 30 | 75.0 (28.5) |
Figure S142. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D10V9.png)
P value = 0.00123 (Fisher's exact test), Q value = 0.012
Table S153. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 19 | 105 | 319 | 5 | 19 | 2 | 2 | 2 | 23 | 1 | 5 | 10 |
subtype1 | 6 | 18 | 39 | 0 | 4 | 1 | 0 | 0 | 4 | 0 | 0 | 0 |
subtype2 | 4 | 39 | 136 | 2 | 4 | 0 | 2 | 0 | 10 | 0 | 1 | 0 |
subtype3 | 6 | 30 | 73 | 3 | 10 | 0 | 0 | 2 | 7 | 1 | 2 | 8 |
subtype4 | 3 | 18 | 71 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 2 |
Figure S143. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D10V10.png)
P value = 0.0407 (Kruskal-Wallis (anova)), Q value = 0.14
Table S154. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 351 | 41.8 (27.4) |
subtype1 | 49 | 43.3 (29.9) |
subtype2 | 136 | 41.4 (25.3) |
subtype3 | 92 | 36.2 (24.3) |
subtype4 | 74 | 48.4 (31.5) |
Figure S144. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D10V11.png)
P value = 0.00525 (Kruskal-Wallis (anova)), Q value = 0.029
Table S155. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 273 | 1964.8 (12.5) |
subtype1 | 47 | 1959.6 (12.7) |
subtype2 | 102 | 1966.3 (12.0) |
subtype3 | 71 | 1964.1 (11.7) |
subtype4 | 53 | 1967.2 (13.1) |
Figure S145. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D10V12.png)
P value = 0.953 (Fisher's exact test), Q value = 1
Table S156. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 339 | 12 | 4 | 26 |
subtype1 | 41 | 1 | 0 | 4 |
subtype2 | 140 | 6 | 1 | 8 |
subtype3 | 90 | 3 | 2 | 9 |
subtype4 | 68 | 2 | 1 | 5 |
Figure S146. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D10V13.png)
P value = 0.317 (Fisher's exact test), Q value = 0.56
Table S157. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 8 | 51 | 386 |
subtype1 | 1 | 6 | 58 |
subtype2 | 4 | 18 | 150 |
subtype3 | 2 | 11 | 114 |
subtype4 | 1 | 16 | 64 |
Figure S147. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'
![](D10V14.png)
P value = 0.758 (Fisher's exact test), Q value = 0.88
Table S158. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 379 |
subtype1 | 0 | 52 |
subtype2 | 3 | 149 |
subtype3 | 2 | 108 |
subtype4 | 2 | 70 |
Figure S148. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'
![](D10V15.png)
Table S159. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 146 | 197 | 93 |
P value = 0.177 (logrank test), Q value = 0.39
Table S160. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 414 | 139 | 0.1 - 238.3 (19.5) |
subtype1 | 137 | 53 | 0.1 - 163.1 (19.3) |
subtype2 | 190 | 55 | 0.1 - 232.2 (19.6) |
subtype3 | 87 | 31 | 0.1 - 238.3 (20.7) |
Figure S149. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D11V1.png)
P value = 0.657 (Kruskal-Wallis (anova)), Q value = 0.8
Table S161. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 408 | 65.1 (10.0) |
subtype1 | 137 | 65.4 (10.7) |
subtype2 | 188 | 64.7 (9.7) |
subtype3 | 83 | 65.4 (9.9) |
Figure S150. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D11V2.png)
P value = 0.432 (Fisher's exact test), Q value = 0.67
Table S162. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 113 | 114 | 1 | 49 | 60 | 64 | 8 | 21 |
subtype1 | 1 | 35 | 34 | 0 | 19 | 18 | 24 | 4 | 10 |
subtype2 | 4 | 59 | 51 | 1 | 23 | 26 | 22 | 3 | 8 |
subtype3 | 0 | 19 | 29 | 0 | 7 | 16 | 18 | 1 | 3 |
Figure S151. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D11V3.png)
P value = 0.0152 (Fisher's exact test), Q value = 0.067
Table S163. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 147 | 231 | 40 | 15 |
subtype1 | 48 | 73 | 14 | 11 |
subtype2 | 76 | 100 | 18 | 2 |
subtype3 | 23 | 58 | 8 | 2 |
Figure S152. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D11V4.png)
P value = 0.188 (Fisher's exact test), Q value = 0.4
Table S164. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 280 | 81 | 65 |
subtype1 | 86 | 34 | 21 |
subtype2 | 138 | 31 | 26 |
subtype3 | 56 | 16 | 18 |
Figure S153. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D11V5.png)
P value = 0.435 (Fisher's exact test), Q value = 0.67
Table S165. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 280 | 19 |
subtype1 | 97 | 9 |
subtype2 | 124 | 8 |
subtype3 | 59 | 2 |
Figure S154. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D11V6.png)
P value = 0.959 (Fisher's exact test), Q value = 1
Table S166. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 233 | 203 |
subtype1 | 77 | 69 |
subtype2 | 105 | 92 |
subtype3 | 51 | 42 |
Figure S155. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D11V7.png)
P value = 0.829 (Fisher's exact test), Q value = 0.92
Table S167. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 311 | 49 |
subtype1 | 102 | 15 |
subtype2 | 145 | 22 |
subtype3 | 64 | 12 |
Figure S156. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D11V8.png)
P value = 0.118 (Kruskal-Wallis (anova)), Q value = 0.29
Table S168. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 113 | 79.6 (28.9) |
subtype1 | 27 | 67.4 (38.9) |
subtype2 | 66 | 84.4 (23.3) |
subtype3 | 20 | 80.0 (26.2) |
Figure S157. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D11V9.png)
P value = 4e-05 (Fisher's exact test), Q value = 9e-04
Table S169. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 17 | 85 | 267 | 5 | 19 | 1 | 2 | 2 | 22 | 1 | 5 | 10 |
subtype1 | 4 | 38 | 75 | 3 | 11 | 1 | 2 | 1 | 7 | 0 | 0 | 4 |
subtype2 | 13 | 39 | 117 | 0 | 7 | 0 | 0 | 0 | 11 | 1 | 5 | 4 |
subtype3 | 0 | 8 | 75 | 2 | 1 | 0 | 0 | 1 | 4 | 0 | 0 | 2 |
Figure S158. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D11V10.png)
P value = 0.00889 (Kruskal-Wallis (anova)), Q value = 0.044
Table S170. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 296 | 41.3 (27.5) |
subtype1 | 104 | 36.2 (26.9) |
subtype2 | 139 | 43.8 (28.0) |
subtype3 | 53 | 44.9 (26.3) |
Figure S159. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D11V11.png)
P value = 0.506 (Kruskal-Wallis (anova)), Q value = 0.68
Table S171. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 235 | 1965.0 (12.3) |
subtype1 | 77 | 1964.6 (13.2) |
subtype2 | 121 | 1966.0 (11.8) |
subtype3 | 37 | 1962.8 (11.9) |
Figure S160. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D11V12.png)
P value = 0.161 (Fisher's exact test), Q value = 0.37
Table S172. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 279 | 9 | 1 | 22 |
subtype1 | 83 | 5 | 1 | 4 |
subtype2 | 139 | 2 | 0 | 15 |
subtype3 | 57 | 2 | 0 | 3 |
Figure S161. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D11V13.png)
P value = 0.949 (Fisher's exact test), Q value = 1
Table S173. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 5 | 49 | 332 |
subtype1 | 1 | 15 | 110 |
subtype2 | 3 | 24 | 148 |
subtype3 | 1 | 10 | 74 |
Figure S162. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'
![](D11V14.png)
P value = 0.101 (Fisher's exact test), Q value = 0.27
Table S174. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 6 | 324 |
subtype1 | 4 | 91 |
subtype2 | 2 | 159 |
subtype3 | 0 | 74 |
Figure S163. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D11V15.png)
Table S175. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 103 | 247 | 86 |
P value = 0.0556 (logrank test), Q value = 0.17
Table S176. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 414 | 139 | 0.1 - 238.3 (19.5) |
subtype1 | 99 | 23 | 0.1 - 164.1 (20.5) |
subtype2 | 233 | 84 | 0.1 - 232.2 (18.9) |
subtype3 | 82 | 32 | 0.1 - 238.3 (19.0) |
Figure S164. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D12V1.png)
P value = 2.92e-06 (Kruskal-Wallis (anova)), Q value = 0.00026
Table S177. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 408 | 65.1 (10.0) |
subtype1 | 98 | 68.3 (8.1) |
subtype2 | 229 | 62.9 (10.4) |
subtype3 | 81 | 67.2 (9.7) |
Figure S165. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D12V2.png)
P value = 0.492 (Fisher's exact test), Q value = 0.68
Table S178. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IV |
---|---|---|---|---|---|---|---|---|---|
ALL | 5 | 113 | 114 | 1 | 49 | 60 | 64 | 8 | 21 |
subtype1 | 1 | 34 | 23 | 1 | 10 | 16 | 9 | 2 | 6 |
subtype2 | 4 | 55 | 67 | 0 | 31 | 36 | 39 | 4 | 11 |
subtype3 | 0 | 24 | 24 | 0 | 8 | 8 | 16 | 2 | 4 |
Figure S166. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D12V3.png)
P value = 0.00927 (Fisher's exact test), Q value = 0.045
Table S179. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 147 | 231 | 40 | 15 |
subtype1 | 43 | 42 | 13 | 4 |
subtype2 | 74 | 143 | 24 | 5 |
subtype3 | 30 | 46 | 3 | 6 |
Figure S167. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D12V4.png)
P value = 0.0721 (Fisher's exact test), Q value = 0.21
Table S180. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 280 | 81 | 65 |
subtype1 | 75 | 15 | 8 |
subtype2 | 155 | 46 | 43 |
subtype3 | 50 | 20 | 14 |
Figure S168. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D12V5.png)
P value = 0.577 (Fisher's exact test), Q value = 0.74
Table S181. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 280 | 19 |
subtype1 | 66 | 6 |
subtype2 | 161 | 11 |
subtype3 | 53 | 2 |
Figure S169. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D12V6.png)
P value = 0.00029 (Fisher's exact test), Q value = 0.0052
Table S182. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 233 | 203 |
subtype1 | 52 | 51 |
subtype2 | 119 | 128 |
subtype3 | 62 | 24 |
Figure S170. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D12V7.png)
P value = 0.187 (Fisher's exact test), Q value = 0.4
Table S183. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 311 | 49 |
subtype1 | 82 | 7 |
subtype2 | 172 | 32 |
subtype3 | 57 | 10 |
Figure S171. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'
![](D12V8.png)
P value = 0.835 (Kruskal-Wallis (anova)), Q value = 0.92
Table S184. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 113 | 79.6 (28.9) |
subtype1 | 22 | 87.3 (16.7) |
subtype2 | 75 | 78.0 (30.4) |
subtype3 | 16 | 76.2 (34.0) |
Figure S172. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D12V9.png)
P value = 3e-05 (Fisher's exact test), Q value = 0.00077
Table S185. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SIGNET RING ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) CARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ALL | 17 | 85 | 267 | 5 | 19 | 1 | 2 | 2 | 22 | 1 | 5 | 10 |
subtype1 | 5 | 27 | 43 | 3 | 6 | 0 | 0 | 2 | 8 | 1 | 1 | 7 |
subtype2 | 9 | 43 | 169 | 0 | 8 | 1 | 0 | 0 | 10 | 0 | 4 | 3 |
subtype3 | 3 | 15 | 55 | 2 | 5 | 0 | 2 | 0 | 4 | 0 | 0 | 0 |
Figure S173. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'
![](D12V10.png)
P value = 0.224 (Kruskal-Wallis (anova)), Q value = 0.44
Table S186. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 296 | 41.3 (27.5) |
subtype1 | 67 | 39.6 (29.0) |
subtype2 | 176 | 43.1 (27.8) |
subtype3 | 53 | 37.6 (24.5) |
Figure S174. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'
![](D12V11.png)
P value = 0.00232 (Kruskal-Wallis (anova)), Q value = 0.016
Table S187. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 235 | 1965.0 (12.3) |
subtype1 | 53 | 1961.2 (10.6) |
subtype2 | 138 | 1967.3 (12.1) |
subtype3 | 44 | 1962.6 (13.5) |
Figure S175. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D12V12.png)
P value = 0.0973 (Fisher's exact test), Q value = 0.26
Table S188. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 279 | 9 | 1 | 22 |
subtype1 | 65 | 0 | 0 | 9 |
subtype2 | 164 | 5 | 1 | 10 |
subtype3 | 50 | 4 | 0 | 3 |
Figure S176. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'
![](D12V13.png)
P value = 0.102 (Fisher's exact test), Q value = 0.27
Table S189. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 5 | 49 | 332 |
subtype1 | 2 | 9 | 81 |
subtype2 | 1 | 34 | 181 |
subtype3 | 2 | 6 | 70 |
Figure S177. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'
![](D12V14.png)
P value = 0.455 (Fisher's exact test), Q value = 0.67
Table S190. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 6 | 324 |
subtype1 | 0 | 82 |
subtype2 | 5 | 182 |
subtype3 | 1 | 60 |
Figure S178. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'
![](D12V15.png)
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Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LUAD-TP/20140677/LUAD-TP.mergedcluster.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUAD-TP/19775350/LUAD-TP.merged_data.txt
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Number of patients = 519
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Number of clustering approaches = 12
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Number of selected clinical features = 15
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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 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 multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.