This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 10 different clustering approaches and 12 clinical features across 290 patients, 55 significant findings detected with P value < 0.05 and Q value < 0.25.
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4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
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CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'PATHOLOGY_T_STAGE'.
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Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', and 'PATHOLOGY_T_STAGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
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Consensus hierarchical 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', 'PATHOLOGY_M_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'PATHOLOGY_N_STAGE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', and 'GENDER'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
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4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 55 significant findings detected.
Clinical Features |
Statistical Tests |
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 |
0.000398 (0.00214) |
0.00175 (0.00777) |
0.7 (0.784) |
0.00228 (0.00978) |
0.000954 (0.00477) |
0.00125 (0.00599) |
0.0432 (0.0943) |
0.332 (0.463) |
5.21e-06 (8e-05) |
4.67e-09 (5.6e-07) |
YEARS TO BIRTH | Kruskal-Wallis (anova) |
0.451 (0.565) |
0.285 (0.416) |
0.00166 (0.00765) |
4.07e-05 (0.000305) |
0.0516 (0.11) |
0.0381 (0.087) |
0.292 (0.422) |
0.0246 (0.0603) |
0.162 (0.291) |
0.203 (0.334) |
PATHOLOGIC STAGE | Fisher's exact test |
1e-05 (8e-05) |
1e-05 (8e-05) |
0.198 (0.334) |
0.00019 (0.00114) |
1e-05 (8e-05) |
1e-05 (8e-05) |
0.00984 (0.0281) |
0.0778 (0.161) |
0.00012 (0.000758) |
1e-05 (8e-05) |
PATHOLOGY T STAGE | Fisher's exact test |
1e-05 (8e-05) |
1e-05 (8e-05) |
0.00344 (0.0121) |
7e-05 (0.000467) |
1e-05 (8e-05) |
1e-05 (8e-05) |
0.0193 (0.0493) |
0.00558 (0.0172) |
0.00041 (0.00214) |
1e-05 (8e-05) |
PATHOLOGY N STAGE | Fisher's exact test |
0.0258 (0.0618) |
7e-05 (0.000467) |
0.243 (0.384) |
0.254 (0.386) |
0.00407 (0.0136) |
1e-05 (8e-05) |
0.0242 (0.0603) |
0.0852 (0.173) |
0.00375 (0.0129) |
0.0024 (0.00993) |
PATHOLOGY M STAGE | Fisher's exact test |
0.0105 (0.0286) |
0.0384 (0.087) |
1 (1.00) |
0.227 (0.364) |
0.124 (0.236) |
0.0414 (0.092) |
0.464 (0.574) |
0.452 (0.565) |
0.0148 (0.0387) |
0.0112 (0.03) |
GENDER | Fisher's exact test |
0.00028 (0.0016) |
0.00288 (0.0111) |
0.757 (0.826) |
0.218 (0.353) |
1e-05 (8e-05) |
1e-05 (8e-05) |
0.11 (0.212) |
0.0103 (0.0286) |
0.00326 (0.0119) |
0.00314 (0.0118) |
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) |
0.00267 (0.0107) |
0.00652 (0.0196) |
0.476 (0.582) |
0.807 (0.865) |
0.00424 (0.0138) |
0.00784 (0.0229) |
0.353 (0.476) |
0.531 (0.625) |
0.00458 (0.0144) |
0.0342 (0.0804) |
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) |
0.385 (0.505) |
0.445 (0.565) |
0.962 (0.987) |
0.9 (0.948) |
0.248 (0.386) |
0.314 (0.443) |
0.988 (1.00) |
0.877 (0.931) |
0.133 (0.245) |
0.342 (0.471) |
YEAR OF TOBACCO SMOKING ONSET | Kruskal-Wallis (anova) |
0.0872 (0.174) |
0.598 (0.677) |
0.776 (0.839) |
0.101 (0.198) |
0.257 (0.386) |
0.518 (0.621) |
0.594 (0.677) |
0.757 (0.826) |
0.527 (0.625) |
0.258 (0.386) |
RACE | Fisher's exact test |
0.2 (0.334) |
0.312 (0.443) |
0.496 (0.601) |
0.054 (0.114) |
0.192 (0.329) |
0.578 (0.667) |
0.387 (0.505) |
0.434 (0.56) |
0.931 (0.972) |
0.356 (0.476) |
ETHNICITY | Fisher's exact test |
0.545 (0.635) |
0.153 (0.278) |
0.175 (0.309) |
0.26 (0.386) |
0.947 (0.979) |
0.189 (0.329) |
0.706 (0.784) |
0.357 (0.476) |
1 (1.00) |
0.129 (0.241) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 113 | 76 | 46 | 52 |
P value = 0.000398 (logrank test), Q value = 0.0021
Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 282 | 44 | 0.1 - 194.8 (24.5) |
subtype1 | 112 | 16 | 0.1 - 129.9 (25.7) |
subtype2 | 74 | 8 | 0.1 - 110.7 (26.3) |
subtype3 | 45 | 5 | 2.2 - 194.8 (25.2) |
subtype4 | 51 | 15 | 0.5 - 99.1 (16.8) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.451 (Kruskal-Wallis (anova)), Q value = 0.57
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 282 | 61.4 (12.1) |
subtype1 | 110 | 62.7 (11.0) |
subtype2 | 75 | 60.3 (11.8) |
subtype3 | 46 | 62.2 (12.1) |
subtype4 | 51 | 59.6 (14.6) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D1V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 171 | 22 | 52 | 15 |
subtype1 | 68 | 10 | 23 | 3 |
subtype2 | 53 | 6 | 7 | 1 |
subtype3 | 34 | 3 | 2 | 2 |
subtype4 | 16 | 3 | 20 | 9 |
Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D1V3.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 190 | 33 | 62 |
subtype1 | 77 | 10 | 24 |
subtype2 | 59 | 10 | 7 |
subtype3 | 36 | 6 | 4 |
subtype4 | 18 | 7 | 27 |
Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D1V4.png)
P value = 0.0258 (Fisher's exact test), Q value = 0.062
Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 48 | 24 | 5 |
subtype1 | 20 | 10 | 1 |
subtype2 | 11 | 1 | 0 |
subtype3 | 7 | 1 | 0 |
subtype4 | 10 | 12 | 4 |
Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D1V5.png)
P value = 0.0105 (Fisher's exact test), Q value = 0.029
Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 93 | 9 |
subtype1 | 42 | 2 |
subtype2 | 22 | 1 |
subtype3 | 14 | 0 |
subtype4 | 15 | 6 |
Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D1V6.png)
P value = 0.00028 (Fisher's exact test), Q value = 0.0016
Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 76 | 211 |
subtype1 | 35 | 78 |
subtype2 | 10 | 66 |
subtype3 | 8 | 38 |
subtype4 | 23 | 29 |
Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D1V7.png)
P value = 0.00267 (Kruskal-Wallis (anova)), Q value = 0.011
Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 76 | 87.5 (22.0) |
subtype1 | 22 | 90.9 (13.4) |
subtype2 | 22 | 94.5 (8.6) |
subtype3 | 12 | 95.8 (7.9) |
subtype4 | 20 | 71.0 (34.6) |
Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D1V8.png)
P value = 0.385 (Kruskal-Wallis (anova)), Q value = 0.5
Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 74 | 32.3 (27.3) |
subtype1 | 25 | 40.9 (36.1) |
subtype2 | 22 | 27.2 (15.5) |
subtype3 | 11 | 25.4 (18.0) |
subtype4 | 16 | 30.8 (28.0) |
Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D1V9.png)
P value = 0.0872 (Kruskal-Wallis (anova)), Q value = 0.17
Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 54 | 1972.5 (15.7) |
subtype1 | 19 | 1978.6 (16.6) |
subtype2 | 16 | 1965.9 (15.5) |
subtype3 | 8 | 1968.6 (13.8) |
subtype4 | 11 | 1974.5 (12.5) |
Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D1V10.png)
P value = 0.2 (Fisher's exact test), Q value = 0.33
Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 61 | 204 |
subtype1 | 0 | 1 | 26 | 82 |
subtype2 | 1 | 1 | 17 | 53 |
subtype3 | 1 | 0 | 6 | 37 |
subtype4 | 0 | 3 | 12 | 32 |
Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D1V11.png)
P value = 0.545 (Fisher's exact test), Q value = 0.64
Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 239 |
subtype1 | 7 | 88 |
subtype2 | 2 | 69 |
subtype3 | 2 | 39 |
subtype4 | 1 | 43 |
Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D1V12.png)
Table S14. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 82 | 71 | 121 |
P value = 0.00175 (logrank test), Q value = 0.0078
Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 269 | 40 | 0.1 - 194.8 (24.0) |
subtype1 | 80 | 11 | 0.4 - 125.3 (26.0) |
subtype2 | 70 | 20 | 0.2 - 194.8 (20.1) |
subtype3 | 119 | 9 | 0.1 - 129.9 (25.1) |
Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.285 (Kruskal-Wallis (anova)), Q value = 0.42
Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 269 | 61.7 (12.1) |
subtype1 | 79 | 62.6 (12.6) |
subtype2 | 70 | 62.3 (13.8) |
subtype3 | 120 | 60.7 (10.7) |
Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D2V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 168 | 19 | 51 | 14 |
subtype1 | 50 | 9 | 11 | 2 |
subtype2 | 23 | 4 | 31 | 10 |
subtype3 | 95 | 6 | 9 | 2 |
Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D2V3.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 186 | 26 | 60 |
subtype1 | 58 | 7 | 15 |
subtype2 | 26 | 8 | 37 |
subtype3 | 102 | 11 | 8 |
Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D2V4.png)
P value = 7e-05 (Fisher's exact test), Q value = 0.00047
Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 47 | 23 | 5 |
subtype1 | 14 | 3 | 1 |
subtype2 | 16 | 20 | 4 |
subtype3 | 17 | 0 | 0 |
Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D2V5.png)
P value = 0.0384 (Fisher's exact test), Q value = 0.087
Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 84 | 8 |
subtype1 | 31 | 1 |
subtype2 | 24 | 6 |
subtype3 | 29 | 1 |
Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D2V6.png)
P value = 0.00288 (Fisher's exact test), Q value = 0.011
Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 73 | 201 |
subtype1 | 22 | 60 |
subtype2 | 29 | 42 |
subtype3 | 22 | 99 |
Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
![](D2V7.png)
P value = 0.00652 (Kruskal-Wallis (anova)), Q value = 0.02
Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 76 | 88.8 (19.6) |
subtype1 | 19 | 86.8 (13.8) |
subtype2 | 14 | 72.9 (36.7) |
subtype3 | 43 | 94.9 (8.0) |
Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D2V8.png)
P value = 0.445 (Kruskal-Wallis (anova)), Q value = 0.57
Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 71 | 32.9 (27.7) |
subtype1 | 23 | 39.0 (36.3) |
subtype2 | 17 | 31.2 (28.0) |
subtype3 | 31 | 29.2 (18.9) |
Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D2V9.png)
P value = 0.598 (Kruskal-Wallis (anova)), Q value = 0.68
Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 53 | 1971.8 (15.7) |
subtype1 | 14 | 1976.4 (18.9) |
subtype2 | 14 | 1970.6 (12.2) |
subtype3 | 25 | 1970.0 (15.6) |
Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D2V10.png)
P value = 0.312 (Fisher's exact test), Q value = 0.44
Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 54 | 198 |
subtype1 | 1 | 3 | 19 | 55 |
subtype2 | 0 | 1 | 17 | 50 |
subtype3 | 1 | 1 | 18 | 93 |
Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'
![](D2V11.png)
P value = 0.153 (Fisher's exact test), Q value = 0.28
Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 226 |
subtype1 | 1 | 70 |
subtype2 | 5 | 51 |
subtype3 | 6 | 105 |
Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'
![](D2V12.png)
Table S27. Description of clustering approach #3: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 75 | 82 | 57 |
P value = 0.7 (logrank test), Q value = 0.78
Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 211 | 32 | 0.1 - 194.8 (24.5) |
subtype1 | 75 | 13 | 0.1 - 103.6 (21.6) |
subtype2 | 81 | 12 | 0.1 - 194.8 (25.6) |
subtype3 | 55 | 7 | 1.1 - 125.3 (27.1) |
Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.00166 (Kruskal-Wallis (anova)), Q value = 0.0077
Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 210 | 61.6 (12.1) |
subtype1 | 74 | 59.2 (12.2) |
subtype2 | 81 | 60.5 (11.9) |
subtype3 | 55 | 66.2 (11.1) |
Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D3V2.png)
P value = 0.198 (Fisher's exact test), Q value = 0.33
Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 129 | 17 | 46 | 14 |
subtype1 | 42 | 8 | 18 | 4 |
subtype2 | 57 | 6 | 11 | 5 |
subtype3 | 30 | 3 | 17 | 5 |
Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D3V3.png)
P value = 0.00344 (Fisher's exact test), Q value = 0.012
Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 136 | 25 | 53 |
subtype1 | 46 | 8 | 21 |
subtype2 | 58 | 14 | 10 |
subtype3 | 32 | 3 | 22 |
Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D3V4.png)
P value = 0.243 (Fisher's exact test), Q value = 0.38
Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 46 | 21 | 4 |
subtype1 | 18 | 7 | 2 |
subtype2 | 19 | 6 | 0 |
subtype3 | 9 | 8 | 2 |
Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D3V5.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 72 | 8 |
subtype1 | 24 | 2 |
subtype2 | 25 | 3 |
subtype3 | 23 | 3 |
Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D3V6.png)
P value = 0.757 (Fisher's exact test), Q value = 0.83
Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 56 | 158 |
subtype1 | 18 | 57 |
subtype2 | 21 | 61 |
subtype3 | 17 | 40 |
Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D3V7.png)
P value = 0.476 (Kruskal-Wallis (anova)), Q value = 0.58
Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 58 | 89.1 (20.8) |
subtype1 | 21 | 87.6 (21.7) |
subtype2 | 26 | 89.2 (22.6) |
subtype3 | 11 | 91.8 (15.4) |
Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D3V8.png)
P value = 0.962 (Kruskal-Wallis (anova)), Q value = 0.99
Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 57 | 31.2 (27.2) |
subtype1 | 20 | 27.5 (15.9) |
subtype2 | 24 | 30.4 (20.3) |
subtype3 | 13 | 38.6 (46.5) |
Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D3V9.png)
P value = 0.776 (Kruskal-Wallis (anova)), Q value = 0.84
Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 45 | 1970.1 (15.2) |
subtype1 | 15 | 1973.5 (16.7) |
subtype2 | 19 | 1968.1 (13.8) |
subtype3 | 11 | 1969.1 (16.0) |
Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D3V10.png)
P value = 0.496 (Fisher's exact test), Q value = 0.6
Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 5 | 45 | 150 |
subtype1 | 0 | 3 | 17 | 47 |
subtype2 | 0 | 2 | 16 | 63 |
subtype3 | 1 | 0 | 12 | 40 |
Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D3V11.png)
P value = 0.175 (Fisher's exact test), Q value = 0.31
Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 176 |
subtype1 | 1 | 58 |
subtype2 | 2 | 75 |
subtype3 | 4 | 43 |
Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D3V12.png)
Table S40. Description of clustering approach #4: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 39 | 67 | 64 | 44 |
P value = 0.00228 (logrank test), Q value = 0.0098
Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 211 | 32 | 0.1 - 194.8 (24.5) |
subtype1 | 38 | 1 | 0.1 - 117.4 (23.1) |
subtype2 | 66 | 9 | 0.1 - 194.8 (24.9) |
subtype3 | 63 | 19 | 0.2 - 103.6 (20.1) |
subtype4 | 44 | 3 | 1.1 - 125.3 (29.4) |
Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 4.07e-05 (Kruskal-Wallis (anova)), Q value = 0.00031
Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 210 | 61.6 (12.1) |
subtype1 | 37 | 62.9 (11.5) |
subtype2 | 66 | 60.3 (11.3) |
subtype3 | 63 | 57.3 (12.9) |
subtype4 | 44 | 68.3 (9.4) |
Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D4V2.png)
P value = 0.00019 (Fisher's exact test), Q value = 0.0011
Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 129 | 17 | 46 | 14 |
subtype1 | 30 | 4 | 4 | 0 |
subtype2 | 48 | 4 | 8 | 4 |
subtype3 | 24 | 5 | 24 | 9 |
subtype4 | 27 | 4 | 10 | 1 |
Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D4V3.png)
P value = 7e-05 (Fisher's exact test), Q value = 0.00047
Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 136 | 25 | 53 |
subtype1 | 31 | 4 | 4 |
subtype2 | 49 | 10 | 8 |
subtype3 | 27 | 7 | 30 |
subtype4 | 29 | 4 | 11 |
Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D4V4.png)
P value = 0.254 (Fisher's exact test), Q value = 0.39
Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 46 | 21 | 4 |
subtype1 | 7 | 1 | 0 |
subtype2 | 14 | 3 | 0 |
subtype3 | 17 | 14 | 3 |
subtype4 | 8 | 3 | 1 |
Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D4V5.png)
P value = 0.227 (Fisher's exact test), Q value = 0.36
Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 72 | 8 |
subtype1 | 10 | 0 |
subtype2 | 19 | 2 |
subtype3 | 27 | 6 |
subtype4 | 16 | 0 |
Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D4V6.png)
P value = 0.218 (Fisher's exact test), Q value = 0.35
Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 56 | 158 |
subtype1 | 8 | 31 |
subtype2 | 16 | 51 |
subtype3 | 23 | 41 |
subtype4 | 9 | 35 |
Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D4V7.png)
P value = 0.807 (Kruskal-Wallis (anova)), Q value = 0.86
Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 58 | 89.1 (20.8) |
subtype1 | 10 | 92.0 (7.9) |
subtype2 | 23 | 88.7 (24.0) |
subtype3 | 17 | 85.9 (25.5) |
subtype4 | 8 | 93.8 (9.2) |
Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D4V8.png)
P value = 0.9 (Kruskal-Wallis (anova)), Q value = 0.95
Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 57 | 31.2 (27.2) |
subtype1 | 9 | 24.8 (11.0) |
subtype2 | 21 | 31.6 (21.5) |
subtype3 | 16 | 29.6 (15.7) |
subtype4 | 11 | 38.4 (51.5) |
Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D4V9.png)
P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.2
Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 45 | 1970.1 (15.2) |
subtype1 | 6 | 1986.2 (14.8) |
subtype2 | 17 | 1967.5 (14.1) |
subtype3 | 12 | 1969.0 (13.5) |
subtype4 | 10 | 1966.4 (14.9) |
Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D4V10.png)
P value = 0.054 (Fisher's exact test), Q value = 0.11
Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 1 | 5 | 45 | 150 |
subtype1 | 0 | 0 | 10 | 25 |
subtype2 | 0 | 0 | 11 | 54 |
subtype3 | 0 | 5 | 14 | 39 |
subtype4 | 1 | 0 | 10 | 32 |
Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D4V11.png)
P value = 0.26 (Fisher's exact test), Q value = 0.39
Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 7 | 176 |
subtype1 | 0 | 33 |
subtype2 | 1 | 59 |
subtype3 | 4 | 48 |
subtype4 | 2 | 36 |
Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D4V12.png)
Table S53. Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 34 | 97 | 67 | 91 |
P value = 0.000954 (logrank test), Q value = 0.0048
Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 284 | 44 | 0.1 - 194.8 (24.7) |
subtype1 | 32 | 6 | 3.8 - 129.9 (30.6) |
subtype2 | 95 | 10 | 0.1 - 117.4 (24.6) |
subtype3 | 67 | 5 | 0.8 - 125.3 (28.8) |
subtype4 | 90 | 23 | 0.2 - 194.8 (19.9) |
Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.0516 (Kruskal-Wallis (anova)), Q value = 0.11
Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 284 | 61.4 (12.1) |
subtype1 | 33 | 58.1 (13.5) |
subtype2 | 96 | 61.5 (10.7) |
subtype3 | 66 | 64.9 (11.3) |
subtype4 | 89 | 60.0 (13.0) |
Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D5V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 172 | 22 | 52 | 15 |
subtype1 | 27 | 2 | 0 | 2 |
subtype2 | 74 | 5 | 8 | 2 |
subtype3 | 38 | 9 | 11 | 0 |
subtype4 | 33 | 6 | 33 | 11 |
Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D5V3.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 192 | 33 | 62 |
subtype1 | 29 | 2 | 2 |
subtype2 | 79 | 11 | 7 |
subtype3 | 46 | 9 | 11 |
subtype4 | 38 | 11 | 42 |
Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D5V4.png)
P value = 0.00407 (Fisher's exact test), Q value = 0.014
Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 49 | 24 | 5 |
subtype1 | 4 | 1 | 1 |
subtype2 | 13 | 1 | 0 |
subtype3 | 15 | 3 | 0 |
subtype4 | 17 | 19 | 4 |
Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D5V5.png)
P value = 0.124 (Fisher's exact test), Q value = 0.24
Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 94 | 9 |
subtype1 | 13 | 1 |
subtype2 | 27 | 1 |
subtype3 | 19 | 0 |
subtype4 | 35 | 7 |
Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D5V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 76 | 213 |
subtype1 | 4 | 30 |
subtype2 | 19 | 78 |
subtype3 | 10 | 57 |
subtype4 | 43 | 48 |
Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D5V7.png)
P value = 0.00424 (Kruskal-Wallis (anova)), Q value = 0.014
Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 87.7 (21.9) |
subtype1 | 8 | 90.0 (7.6) |
subtype2 | 32 | 96.2 (7.1) |
subtype3 | 17 | 85.3 (21.2) |
subtype4 | 20 | 75.0 (33.8) |
Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D5V8.png)
P value = 0.248 (Kruskal-Wallis (anova)), Q value = 0.39
Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 32.1 (27.2) |
subtype1 | 8 | 37.8 (21.1) |
subtype2 | 28 | 26.2 (16.7) |
subtype3 | 18 | 27.5 (19.7) |
subtype4 | 21 | 41.7 (41.1) |
Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D5V9.png)
P value = 0.257 (Kruskal-Wallis (anova)), Q value = 0.39
Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 55 | 1972.3 (15.6) |
subtype1 | 3 | 1967.7 (18.5) |
subtype2 | 24 | 1968.7 (14.6) |
subtype3 | 11 | 1972.5 (17.6) |
subtype4 | 17 | 1978.0 (14.9) |
Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D5V10.png)
P value = 0.192 (Fisher's exact test), Q value = 0.33
Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 61 | 206 |
subtype1 | 0 | 1 | 8 | 22 |
subtype2 | 1 | 0 | 19 | 71 |
subtype3 | 1 | 0 | 11 | 53 |
subtype4 | 0 | 4 | 23 | 60 |
Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D5V11.png)
P value = 0.947 (Fisher's exact test), Q value = 0.98
Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 241 |
subtype1 | 1 | 26 |
subtype2 | 5 | 85 |
subtype3 | 2 | 59 |
subtype4 | 4 | 71 |
Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D5V12.png)
Table S66. Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 118 | 125 | 46 |
P value = 0.00125 (logrank test), Q value = 0.006
Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 284 | 44 | 0.1 - 194.8 (24.7) |
subtype1 | 117 | 19 | 0.2 - 194.8 (24.5) |
subtype2 | 123 | 12 | 0.1 - 129.9 (25.8) |
subtype3 | 44 | 13 | 0.2 - 92.6 (20.2) |
Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.0381 (Kruskal-Wallis (anova)), Q value = 0.087
Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 284 | 61.4 (12.1) |
subtype1 | 115 | 63.6 (11.7) |
subtype2 | 124 | 59.7 (11.1) |
subtype3 | 45 | 60.3 (14.6) |
Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D6V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 172 | 22 | 52 | 15 |
subtype1 | 56 | 12 | 34 | 5 |
subtype2 | 94 | 8 | 8 | 2 |
subtype3 | 22 | 2 | 10 | 8 |
Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D6V3.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 192 | 33 | 62 |
subtype1 | 65 | 14 | 37 |
subtype2 | 103 | 15 | 7 |
subtype3 | 24 | 4 | 18 |
Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D6V4.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 49 | 24 | 5 |
subtype1 | 25 | 14 | 2 |
subtype2 | 20 | 0 | 0 |
subtype3 | 4 | 10 | 3 |
Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D6V5.png)
P value = 0.0414 (Fisher's exact test), Q value = 0.092
Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 94 | 9 |
subtype1 | 41 | 3 |
subtype2 | 36 | 1 |
subtype3 | 17 | 5 |
Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D6V6.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 76 | 213 |
subtype1 | 29 | 89 |
subtype2 | 22 | 103 |
subtype3 | 25 | 21 |
Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D6V7.png)
P value = 0.00784 (Kruskal-Wallis (anova)), Q value = 0.023
Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 87.7 (21.9) |
subtype1 | 28 | 82.5 (26.9) |
subtype2 | 42 | 94.8 (7.7) |
subtype3 | 7 | 65.7 (37.4) |
Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D6V8.png)
P value = 0.314 (Kruskal-Wallis (anova)), Q value = 0.44
Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 32.1 (27.2) |
subtype1 | 37 | 34.6 (34.2) |
subtype2 | 30 | 27.9 (19.0) |
subtype3 | 8 | 36.0 (13.2) |
Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D6V9.png)
P value = 0.518 (Kruskal-Wallis (anova)), Q value = 0.62
Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 55 | 1972.3 (15.6) |
subtype1 | 25 | 1974.0 (16.0) |
subtype2 | 24 | 1969.5 (15.5) |
subtype3 | 6 | 1976.2 (15.6) |
Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D6V10.png)
P value = 0.578 (Fisher's exact test), Q value = 0.67
Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 61 | 206 |
subtype1 | 1 | 2 | 24 | 88 |
subtype2 | 1 | 1 | 25 | 90 |
subtype3 | 0 | 2 | 12 | 28 |
Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D6V11.png)
P value = 0.189 (Fisher's exact test), Q value = 0.33
Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 241 |
subtype1 | 4 | 100 |
subtype2 | 4 | 109 |
subtype3 | 4 | 32 |
Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D6V12.png)
Table S79. Description of clustering approach #7: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 91 | 110 | 89 |
P value = 0.0432 (logrank test), Q value = 0.094
Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 285 | 44 | 0.1 - 194.8 (24.6) |
subtype1 | 90 | 11 | 0.4 - 125.3 (26.7) |
subtype2 | 106 | 11 | 0.1 - 123.6 (22.8) |
subtype3 | 89 | 22 | 0.1 - 194.8 (23.3) |
Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.292 (Kruskal-Wallis (anova)), Q value = 0.42
Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 285 | 61.5 (12.1) |
subtype1 | 90 | 62.7 (11.7) |
subtype2 | 106 | 62.2 (10.9) |
subtype3 | 89 | 59.4 (13.7) |
Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D7V2.png)
P value = 0.00984 (Fisher's exact test), Q value = 0.028
Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 173 | 22 | 52 | 15 |
subtype1 | 46 | 12 | 19 | 2 |
subtype2 | 77 | 5 | 14 | 4 |
subtype3 | 50 | 5 | 19 | 9 |
Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D7V3.png)
P value = 0.0193 (Fisher's exact test), Q value = 0.049
Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 193 | 33 | 62 |
subtype1 | 54 | 16 | 20 |
subtype2 | 84 | 10 | 16 |
subtype3 | 55 | 7 | 26 |
Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D7V4.png)
P value = 0.0242 (Fisher's exact test), Q value = 0.06
Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 49 | 24 | 5 |
subtype1 | 21 | 5 | 1 |
subtype2 | 14 | 4 | 0 |
subtype3 | 14 | 15 | 4 |
Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D7V5.png)
P value = 0.464 (Fisher's exact test), Q value = 0.57
Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 94 | 9 |
subtype1 | 30 | 1 |
subtype2 | 28 | 3 |
subtype3 | 36 | 5 |
Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D7V6.png)
P value = 0.11 (Fisher's exact test), Q value = 0.21
Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 77 | 213 |
subtype1 | 18 | 73 |
subtype2 | 29 | 81 |
subtype3 | 30 | 59 |
Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
![](D7V7.png)
P value = 0.353 (Kruskal-Wallis (anova)), Q value = 0.48
Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 87.7 (21.9) |
subtype1 | 21 | 83.8 (24.6) |
subtype2 | 30 | 93.7 (8.1) |
subtype3 | 26 | 83.8 (28.9) |
Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D7V8.png)
P value = 0.988 (Kruskal-Wallis (anova)), Q value = 1
Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 76 | 31.7 (27.2) |
subtype1 | 30 | 31.4 (24.4) |
subtype2 | 24 | 33.6 (35.9) |
subtype3 | 22 | 30.2 (19.8) |
Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D7V9.png)
P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.68
Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 1972.2 (15.5) |
subtype1 | 18 | 1970.4 (15.3) |
subtype2 | 22 | 1975.4 (15.6) |
subtype3 | 16 | 1969.7 (15.8) |
Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D7V10.png)
P value = 0.387 (Fisher's exact test), Q value = 0.5
Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 61 | 207 |
subtype1 | 0 | 1 | 24 | 63 |
subtype2 | 1 | 2 | 16 | 83 |
subtype3 | 1 | 2 | 21 | 61 |
Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'
![](D7V11.png)
P value = 0.706 (Fisher's exact test), Q value = 0.78
Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 242 |
subtype1 | 3 | 79 |
subtype2 | 4 | 92 |
subtype3 | 5 | 71 |
Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'
![](D7V12.png)
Table S92. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 121 | 81 | 88 |
P value = 0.332 (logrank test), Q value = 0.46
Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 285 | 44 | 0.1 - 194.8 (24.6) |
subtype1 | 119 | 17 | 0.4 - 125.3 (26.4) |
subtype2 | 79 | 9 | 0.1 - 110.7 (22.1) |
subtype3 | 87 | 18 | 0.1 - 194.8 (24.5) |
Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.0246 (Kruskal-Wallis (anova)), Q value = 0.06
Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 285 | 61.5 (12.1) |
subtype1 | 119 | 63.1 (11.6) |
subtype2 | 80 | 62.8 (11.0) |
subtype3 | 86 | 58.0 (13.2) |
Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D8V2.png)
P value = 0.0778 (Fisher's exact test), Q value = 0.16
Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 173 | 22 | 52 | 15 |
subtype1 | 67 | 11 | 23 | 4 |
subtype2 | 55 | 7 | 9 | 2 |
subtype3 | 51 | 4 | 20 | 9 |
Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D8V3.png)
P value = 0.00558 (Fisher's exact test), Q value = 0.017
Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 193 | 33 | 62 |
subtype1 | 77 | 14 | 28 |
subtype2 | 62 | 12 | 7 |
subtype3 | 54 | 7 | 27 |
Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D8V4.png)
P value = 0.0852 (Fisher's exact test), Q value = 0.17
Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 49 | 24 | 5 |
subtype1 | 23 | 8 | 2 |
subtype2 | 12 | 2 | 0 |
subtype3 | 14 | 14 | 3 |
Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D8V5.png)
P value = 0.452 (Fisher's exact test), Q value = 0.57
Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 94 | 9 |
subtype1 | 38 | 3 |
subtype2 | 24 | 1 |
subtype3 | 32 | 5 |
Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D8V6.png)
P value = 0.0103 (Fisher's exact test), Q value = 0.029
Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 77 | 213 |
subtype1 | 27 | 94 |
subtype2 | 16 | 65 |
subtype3 | 34 | 54 |
Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
![](D8V7.png)
P value = 0.531 (Kruskal-Wallis (anova)), Q value = 0.62
Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 87.7 (21.9) |
subtype1 | 29 | 86.2 (27.4) |
subtype2 | 24 | 92.1 (9.3) |
subtype3 | 24 | 85.0 (23.6) |
Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D8V8.png)
P value = 0.877 (Kruskal-Wallis (anova)), Q value = 0.93
Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 76 | 31.7 (27.2) |
subtype1 | 36 | 32.1 (23.7) |
subtype2 | 16 | 27.5 (17.4) |
subtype3 | 24 | 34.1 (36.6) |
Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D8V9.png)
P value = 0.757 (Kruskal-Wallis (anova)), Q value = 0.83
Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 1972.2 (15.5) |
subtype1 | 23 | 1970.3 (14.0) |
subtype2 | 15 | 1971.9 (15.3) |
subtype3 | 18 | 1974.8 (17.9) |
Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D8V10.png)
P value = 0.434 (Fisher's exact test), Q value = 0.56
Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 61 | 207 |
subtype1 | 1 | 1 | 28 | 86 |
subtype2 | 1 | 1 | 12 | 61 |
subtype3 | 0 | 3 | 21 | 60 |
Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'
![](D8V11.png)
P value = 0.357 (Fisher's exact test), Q value = 0.48
Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 12 | 242 |
subtype1 | 4 | 103 |
subtype2 | 2 | 71 |
subtype3 | 6 | 68 |
Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'
![](D8V12.png)
Table S105. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 70 | 83 | 70 |
P value = 5.21e-06 (logrank test), Q value = 8e-05
Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 220 | 37 | 0.1 - 194.8 (24.5) |
subtype1 | 69 | 7 | 0.4 - 106.5 (22.5) |
subtype2 | 82 | 5 | 0.1 - 123.6 (26.3) |
subtype3 | 69 | 25 | 0.2 - 194.8 (20.3) |
Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.162 (Kruskal-Wallis (anova)), Q value = 0.29
Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 219 | 60.8 (12.2) |
subtype1 | 67 | 62.7 (10.8) |
subtype2 | 83 | 59.2 (11.3) |
subtype3 | 69 | 61.1 (14.3) |
Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D9V2.png)
P value = 0.00012 (Fisher's exact test), Q value = 0.00076
Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 132 | 16 | 43 | 14 |
subtype1 | 39 | 6 | 14 | 2 |
subtype2 | 63 | 4 | 10 | 1 |
subtype3 | 30 | 6 | 19 | 11 |
Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D9V3.png)
P value = 0.00041 (Fisher's exact test), Q value = 0.0021
Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 146 | 21 | 54 |
subtype1 | 45 | 8 | 17 |
subtype2 | 67 | 7 | 9 |
subtype3 | 34 | 6 | 28 |
Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D9V4.png)
P value = 0.00375 (Fisher's exact test), Q value = 0.013
Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 40 | 23 | 5 |
subtype1 | 17 | 4 | 1 |
subtype2 | 13 | 3 | 0 |
subtype3 | 10 | 16 | 4 |
Figure S101. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D9V5.png)
P value = 0.0148 (Fisher's exact test), Q value = 0.039
Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 75 | 8 |
subtype1 | 24 | 1 |
subtype2 | 25 | 0 |
subtype3 | 26 | 7 |
Figure S102. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D9V6.png)
P value = 0.00326 (Fisher's exact test), Q value = 0.012
Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 62 | 161 |
subtype1 | 19 | 51 |
subtype2 | 14 | 69 |
subtype3 | 29 | 41 |
Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D9V7.png)
P value = 0.00458 (Kruskal-Wallis (anova)), Q value = 0.014
Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 65 | 88.5 (21.4) |
subtype1 | 16 | 85.6 (19.3) |
subtype2 | 32 | 96.2 (6.1) |
subtype3 | 17 | 76.5 (33.5) |
Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D9V8.png)
P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.24
Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 59 | 33.8 (29.0) |
subtype1 | 16 | 43.0 (46.6) |
subtype2 | 26 | 25.9 (16.6) |
subtype3 | 17 | 37.1 (19.7) |
Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D9V9.png)
P value = 0.527 (Kruskal-Wallis (anova)), Q value = 0.62
Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 45 | 1970.8 (16.4) |
subtype1 | 15 | 1971.6 (17.7) |
subtype2 | 22 | 1968.5 (16.3) |
subtype3 | 8 | 1975.9 (15.0) |
Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D9V10.png)
P value = 0.931 (Fisher's exact test), Q value = 0.97
Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 40 | 166 |
subtype1 | 1 | 1 | 11 | 54 |
subtype2 | 1 | 2 | 14 | 62 |
subtype3 | 0 | 2 | 15 | 50 |
Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D9V11.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 10 | 183 |
subtype1 | 3 | 58 |
subtype2 | 4 | 71 |
subtype3 | 3 | 54 |
Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D9V12.png)
Table S118. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 105 | 75 | 14 | 29 |
P value = 4.67e-09 (logrank test), Q value = 5.6e-07
Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 220 | 37 | 0.1 - 194.8 (24.5) |
subtype1 | 103 | 9 | 0.5 - 123.6 (26.2) |
subtype2 | 75 | 22 | 0.4 - 194.8 (24.5) |
subtype3 | 13 | 6 | 0.2 - 75.4 (7.9) |
subtype4 | 29 | 0 | 0.1 - 87.1 (21.6) |
Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.203 (Kruskal-Wallis (anova)), Q value = 0.33
Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 219 | 60.8 (12.2) |
subtype1 | 104 | 61.9 (11.3) |
subtype2 | 73 | 61.6 (11.8) |
subtype3 | 13 | 52.5 (18.5) |
subtype4 | 29 | 58.7 (12.1) |
Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D10V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 132 | 16 | 43 | 14 |
subtype1 | 69 | 11 | 13 | 2 |
subtype2 | 34 | 3 | 23 | 9 |
subtype3 | 3 | 2 | 6 | 3 |
subtype4 | 26 | 0 | 1 | 0 |
Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'
![](D10V3.png)
P value = 1e-05 (Fisher's exact test), Q value = 8e-05
Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 146 | 21 | 54 |
subtype1 | 78 | 12 | 13 |
subtype2 | 38 | 5 | 32 |
subtype3 | 3 | 3 | 8 |
subtype4 | 27 | 1 | 1 |
Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'
![](D10V4.png)
P value = 0.0024 (Fisher's exact test), Q value = 0.0099
Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 40 | 23 | 5 |
subtype1 | 18 | 4 | 0 |
subtype2 | 18 | 12 | 5 |
subtype3 | 1 | 7 | 0 |
subtype4 | 3 | 0 | 0 |
Figure S113. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'
![](D10V5.png)
P value = 0.0112 (Fisher's exact test), Q value = 0.03
Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 75 | 8 |
subtype1 | 28 | 0 |
subtype2 | 33 | 5 |
subtype3 | 5 | 3 |
subtype4 | 9 | 0 |
Figure S114. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'
![](D10V6.png)
P value = 0.00314 (Fisher's exact test), Q value = 0.012
Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 62 | 161 |
subtype1 | 20 | 85 |
subtype2 | 25 | 50 |
subtype3 | 9 | 5 |
subtype4 | 8 | 21 |
Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D10V7.png)
P value = 0.0342 (Kruskal-Wallis (anova)), Q value = 0.08
Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 65 | 88.5 (21.4) |
subtype1 | 32 | 93.4 (7.9) |
subtype2 | 22 | 79.1 (31.8) |
subtype3 | 1 | 40.0 (NA) |
subtype4 | 10 | 98.0 (4.2) |
Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'
![](D10V8.png)
P value = 0.342 (Kruskal-Wallis (anova)), Q value = 0.47
Table S127. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 59 | 33.8 (29.0) |
subtype1 | 23 | 34.0 (25.2) |
subtype2 | 20 | 40.2 (39.1) |
subtype4 | 16 | 25.4 (16.0) |
Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'
![](D10V9.png)
P value = 0.258 (Kruskal-Wallis (anova)), Q value = 0.39
Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 45 | 1970.8 (16.4) |
subtype1 | 17 | 1967.2 (13.5) |
subtype2 | 15 | 1976.5 (18.2) |
subtype4 | 13 | 1969.2 (17.3) |
Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'
![](D10V10.png)
P value = 0.356 (Fisher's exact test), Q value = 0.48
Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|---|
ALL | 2 | 5 | 40 | 166 |
subtype1 | 2 | 1 | 16 | 82 |
subtype2 | 0 | 3 | 15 | 55 |
subtype3 | 0 | 1 | 4 | 7 |
subtype4 | 0 | 0 | 5 | 22 |
Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D10V11.png)
P value = 0.129 (Fisher's exact test), Q value = 0.24
Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO |
---|---|---|
ALL | 10 | 183 |
subtype1 | 2 | 91 |
subtype2 | 4 | 57 |
subtype3 | 1 | 10 |
subtype4 | 3 | 25 |
Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'
![](D10V12.png)
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Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIRP-TP/20139337/KIRP-TP.mergedcluster.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRP-TP/19775272/KIRP-TP.merged_data.txt
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Number of patients = 290
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Number of clustering approaches = 10
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Number of selected clinical features = 12
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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.