This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 9 clinical features across 84 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.
-
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
-
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGY.M.STAGE', and 'HISTOLOGICAL.TYPE'.
-
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.
-
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
-
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
-
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
-
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 6 significant findings detected.
Clinical Features |
Statistical Tests |
Copy Number Ratio CNMF subtypes |
METHLYATION CNMF |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
MIRseq Mature CNMF subtypes |
MIRseq Mature cHierClus subtypes |
Time to Death | logrank test |
0.0568 (1.00) |
0.00168 (0.114) |
0.262 (1.00) |
0.721 (1.00) |
0.567 (1.00) |
0.208 (1.00) |
0.455 (1.00) |
0.704 (1.00) |
AGE | ANOVA |
0.878 (1.00) |
0.264 (1.00) |
0.246 (1.00) |
0.235 (1.00) |
0.819 (1.00) |
0.202 (1.00) |
0.6 (1.00) |
0.957 (1.00) |
PATHOLOGY T STAGE | Fisher's exact test |
0.928 (1.00) |
0.465 (1.00) |
0.812 (1.00) |
0.892 (1.00) |
0.931 (1.00) |
0.195 (1.00) |
0.396 (1.00) |
0.575 (1.00) |
PATHOLOGY N STAGE | Fisher's exact test |
0.48 (1.00) |
0.641 (1.00) |
0.569 (1.00) |
0.928 (1.00) |
0.802 (1.00) |
0.825 (1.00) |
0.635 (1.00) |
0.319 (1.00) |
PATHOLOGY M STAGE | Chi-square test |
0.93 (1.00) |
0.00172 (0.115) |
0.0399 (1.00) |
0.0875 (1.00) |
0.15 (1.00) |
0.424 (1.00) |
0.122 (1.00) |
0.183 (1.00) |
HISTOLOGICAL TYPE | Chi-square test |
0.656 (1.00) |
0.000388 (0.0268) |
0.00432 (0.281) |
8.92e-08 (6.43e-06) |
0.00809 (0.518) |
4.21e-06 (0.000299) |
0.0039 (0.257) |
2.96e-05 (0.00207) |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test |
0.437 (1.00) |
0.222 (1.00) |
0.0832 (1.00) |
0.317 (1.00) |
0.297 (1.00) |
0.0816 (1.00) |
0.113 (1.00) |
0.406 (1.00) |
NUMBERPACKYEARSSMOKED | ANOVA |
0.725 (1.00) |
0.464 (1.00) |
0.872 (1.00) |
0.598 (1.00) |
0.871 (1.00) |
0.496 (1.00) |
0.939 (1.00) |
0.607 (1.00) |
NUMBER OF LYMPH NODES | ANOVA |
0.266 (1.00) |
0.0844 (1.00) |
0.392 (1.00) |
0.552 (1.00) |
0.602 (1.00) |
0.514 (1.00) |
0.551 (1.00) |
0.478 (1.00) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 27 | 14 | 37 |
P value = 0.0568 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 76 | 11 | 0.0 - 177.0 (6.0) |
subtype1 | 25 | 2 | 0.0 - 117.4 (6.0) |
subtype2 | 14 | 6 | 1.2 - 87.8 (18.1) |
subtype3 | 37 | 3 | 0.1 - 177.0 (3.7) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.878 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 47.6 (13.0) |
subtype1 | 26 | 48.4 (11.7) |
subtype2 | 14 | 46.2 (12.6) |
subtype3 | 37 | 47.5 (14.2) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.928 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 49 | 23 |
subtype1 | 17 | 8 |
subtype2 | 7 | 4 |
subtype3 | 25 | 11 |
Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.48 (Fisher's exact test), Q value = 1
Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 49 | 21 |
subtype1 | 18 | 6 |
subtype2 | 6 | 5 |
subtype3 | 25 | 10 |
Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.93 (Chi-square test), Q value = 1
Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 45 | 2 | 26 |
subtype1 | 17 | 1 | 8 |
subtype2 | 7 | 0 | 5 |
subtype3 | 21 | 1 | 13 |
Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 0.656 (Chi-square test), Q value = 1
Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 66 | 1 | 8 | 1 | 2 |
subtype1 | 25 | 0 | 1 | 0 | 1 |
subtype2 | 13 | 0 | 1 | 0 | 0 |
subtype3 | 28 | 1 | 6 | 1 | 1 |
Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.437 (Fisher's exact test), Q value = 1
Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 65 |
subtype1 | 4 | 23 |
subtype2 | 4 | 10 |
subtype3 | 5 | 32 |
Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.725 (ANOVA), Q value = 1
Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 18.0 (12.8) |
subtype1 | 7 | 16.4 (11.0) |
subtype2 | 4 | 22.8 (10.0) |
subtype3 | 10 | 17.2 (15.4) |
Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.266 (ANOVA), Q value = 1
Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 64 | 0.6 (1.3) |
subtype1 | 23 | 0.4 (0.9) |
subtype2 | 10 | 1.2 (1.6) |
subtype3 | 31 | 0.5 (1.3) |
Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S11. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Number of samples | 17 | 12 | 15 | 16 | 13 | 7 | 4 |
P value = 0.00168 (logrank test), Q value = 0.11
Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 82 | 14 | 0.0 - 177.0 (6.9) |
subtype1 | 17 | 0 | 0.1 - 177.0 (13.9) |
subtype2 | 12 | 3 | 0.1 - 95.1 (11.9) |
subtype3 | 14 | 1 | 0.0 - 67.5 (5.0) |
subtype4 | 15 | 8 | 0.1 - 69.9 (5.5) |
subtype5 | 13 | 2 | 0.3 - 124.3 (3.7) |
subtype6 | 7 | 0 | 1.7 - 147.3 (29.7) |
subtype7 | 4 | 0 | 0.6 - 53.1 (11.7) |
Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.264 (ANOVA), Q value = 1
Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 83 | 48.2 (13.2) |
subtype1 | 17 | 43.6 (10.3) |
subtype2 | 12 | 45.4 (13.0) |
subtype3 | 15 | 55.6 (12.8) |
subtype4 | 15 | 48.0 (16.4) |
subtype5 | 13 | 47.0 (11.8) |
subtype6 | 7 | 49.1 (10.0) |
subtype7 | 4 | 51.5 (19.5) |
Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.465 (Chi-square test), Q value = 1
Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 53 | 25 |
subtype1 | 11 | 6 |
subtype2 | 8 | 3 |
subtype3 | 7 | 6 |
subtype4 | 11 | 2 |
subtype5 | 10 | 3 |
subtype6 | 3 | 4 |
subtype7 | 3 | 1 |
Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.641 (Chi-square test), Q value = 1
Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 52 | 24 |
subtype1 | 12 | 5 |
subtype2 | 8 | 3 |
subtype3 | 8 | 4 |
subtype4 | 6 | 7 |
subtype5 | 10 | 3 |
subtype6 | 5 | 1 |
subtype7 | 3 | 1 |
Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.00172 (Chi-square test), Q value = 0.12
Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 51 | 2 | 26 |
subtype1 | 12 | 0 | 5 |
subtype2 | 10 | 0 | 2 |
subtype3 | 5 | 0 | 9 |
subtype4 | 10 | 0 | 2 |
subtype5 | 8 | 0 | 5 |
subtype6 | 4 | 2 | 1 |
subtype7 | 2 | 0 | 2 |
Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 0.000388 (Chi-square test), Q value = 0.027
Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 72 | 1 | 8 | 1 | 2 |
subtype1 | 17 | 0 | 0 | 0 | 0 |
subtype2 | 12 | 0 | 0 | 0 | 0 |
subtype3 | 15 | 0 | 0 | 0 | 0 |
subtype4 | 16 | 0 | 0 | 0 | 0 |
subtype5 | 4 | 1 | 5 | 1 | 2 |
subtype6 | 4 | 0 | 3 | 0 | 0 |
subtype7 | 4 | 0 | 0 | 0 | 0 |
Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.222 (Chi-square test), Q value = 1
Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 17 | 67 |
subtype1 | 3 | 14 |
subtype2 | 4 | 8 |
subtype3 | 2 | 13 |
subtype4 | 5 | 11 |
subtype5 | 1 | 12 |
subtype6 | 0 | 7 |
subtype7 | 2 | 2 |
Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.464 (ANOVA), Q value = 1
Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 18.0 (12.8) |
subtype1 | 5 | 14.0 (4.2) |
subtype2 | 1 | 20.0 (NA) |
subtype3 | 6 | 22.4 (17.8) |
subtype4 | 4 | 14.0 (8.4) |
subtype5 | 2 | 18.8 (23.0) |
subtype6 | 2 | 25.0 (21.2) |
subtype7 | 1 | 10.0 (NA) |
Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.0844 (ANOVA), Q value = 1
Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 70 | 0.9 (2.4) |
subtype1 | 12 | 0.2 (0.6) |
subtype2 | 11 | 0.9 (1.9) |
subtype3 | 13 | 0.7 (1.3) |
subtype4 | 12 | 2.9 (4.8) |
subtype5 | 12 | 0.5 (1.2) |
subtype6 | 6 | 0.2 (0.4) |
subtype7 | 4 | 0.2 (0.5) |
Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S21. Description of clustering approach #3: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 23 | 13 | 10 | 15 | 15 |
P value = 0.262 (logrank test), Q value = 1
Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 74 | 12 | 0.1 - 177.0 (9.7) |
subtype1 | 23 | 3 | 0.1 - 177.0 (2.4) |
subtype2 | 12 | 3 | 0.3 - 36.8 (10.3) |
subtype3 | 10 | 1 | 0.1 - 101.8 (14.4) |
subtype4 | 14 | 4 | 0.1 - 95.1 (13.4) |
subtype5 | 15 | 1 | 1.2 - 147.3 (29.7) |
Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.246 (ANOVA), Q value = 1
Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 47.3 (12.8) |
subtype1 | 23 | 49.1 (15.5) |
subtype2 | 13 | 51.5 (9.9) |
subtype3 | 10 | 49.1 (11.2) |
subtype4 | 14 | 41.2 (12.1) |
subtype5 | 15 | 45.6 (11.2) |
Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.812 (Chi-square test), Q value = 1
Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 48 | 24 |
subtype1 | 14 | 8 |
subtype2 | 7 | 5 |
subtype3 | 6 | 4 |
subtype4 | 10 | 3 |
subtype5 | 11 | 4 |
Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.569 (Chi-square test), Q value = 1
Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 48 | 23 |
subtype1 | 16 | 6 |
subtype2 | 7 | 5 |
subtype3 | 5 | 5 |
subtype4 | 9 | 4 |
subtype5 | 11 | 3 |
Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.0399 (Chi-square test), Q value = 1
Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 46 | 2 | 23 |
subtype1 | 14 | 0 | 7 |
subtype2 | 9 | 0 | 3 |
subtype3 | 4 | 0 | 6 |
subtype4 | 12 | 0 | 1 |
subtype5 | 7 | 2 | 6 |
Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 0.00432 (Chi-square test), Q value = 0.28
Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 65 | 1 | 8 | 1 | 1 |
subtype1 | 23 | 0 | 0 | 0 | 0 |
subtype2 | 11 | 0 | 2 | 0 | 0 |
subtype3 | 10 | 0 | 0 | 0 | 0 |
subtype4 | 15 | 0 | 0 | 0 | 0 |
subtype5 | 6 | 1 | 6 | 1 | 1 |
Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.0832 (Chi-square test), Q value = 1
Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 16 | 60 |
subtype1 | 2 | 21 |
subtype2 | 4 | 9 |
subtype3 | 4 | 6 |
subtype4 | 5 | 10 |
subtype5 | 1 | 14 |
Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.872 (ANOVA), Q value = 1
Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 7 | 21.4 (15.7) |
subtype2 | 3 | 24.0 (11.5) |
subtype3 | 2 | 12.5 (3.5) |
subtype4 | 3 | 18.7 (7.1) |
subtype5 | 4 | 15.6 (16.6) |
Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.392 (ANOVA), Q value = 1
Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 64 | 0.9 (2.3) |
subtype1 | 20 | 0.7 (1.6) |
subtype2 | 11 | 2.1 (4.8) |
subtype3 | 8 | 1.1 (1.5) |
subtype4 | 11 | 0.5 (0.8) |
subtype5 | 14 | 0.4 (1.1) |
Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S31. Description of clustering approach #4: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 16 | 9 | 51 |
P value = 0.721 (logrank test), Q value = 1
Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 74 | 12 | 0.1 - 177.0 (9.7) |
subtype1 | 16 | 2 | 0.3 - 124.3 (11.2) |
subtype2 | 9 | 1 | 0.1 - 78.7 (11.4) |
subtype3 | 49 | 9 | 0.1 - 177.0 (6.8) |
Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.235 (ANOVA), Q value = 1
Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 75 | 47.3 (12.8) |
subtype1 | 16 | 48.9 (11.0) |
subtype2 | 9 | 40.6 (9.8) |
subtype3 | 50 | 48.1 (13.6) |
Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.892 (Fisher's exact test), Q value = 1
Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 48 | 24 |
subtype1 | 10 | 6 |
subtype2 | 6 | 3 |
subtype3 | 32 | 15 |
Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.928 (Fisher's exact test), Q value = 1
Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 48 | 23 |
subtype1 | 11 | 4 |
subtype2 | 6 | 3 |
subtype3 | 31 | 16 |
Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.0875 (Chi-square test), Q value = 1
Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 46 | 2 | 23 |
subtype1 | 8 | 2 | 6 |
subtype2 | 7 | 0 | 2 |
subtype3 | 31 | 0 | 15 |
Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 8.92e-08 (Chi-square test), Q value = 6.4e-06
Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 65 | 1 | 8 | 1 | 1 |
subtype1 | 5 | 1 | 8 | 1 | 1 |
subtype2 | 9 | 0 | 0 | 0 | 0 |
subtype3 | 51 | 0 | 0 | 0 | 0 |
Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.317 (Fisher's exact test), Q value = 1
Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 16 | 60 |
subtype1 | 1 | 15 |
subtype2 | 2 | 7 |
subtype3 | 13 | 38 |
Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.598 (ANOVA), Q value = 1
Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 3 | 25.8 (20.4) |
subtype2 | 2 | 15.0 (7.1) |
subtype3 | 14 | 18.4 (12.0) |
Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.552 (ANOVA), Q value = 1
Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 64 | 0.9 (2.3) |
subtype1 | 14 | 0.5 (1.1) |
subtype2 | 8 | 0.4 (0.7) |
subtype3 | 42 | 1.1 (2.7) |
Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S41. Description of clustering approach #5: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 29 | 29 | 20 |
P value = 0.567 (logrank test), Q value = 1
Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 76 | 14 | 0.1 - 177.0 (10.1) |
subtype1 | 28 | 7 | 0.1 - 177.0 (11.9) |
subtype2 | 28 | 5 | 0.1 - 147.3 (7.4) |
subtype3 | 20 | 2 | 0.1 - 124.3 (10.2) |
Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.819 (ANOVA), Q value = 1
Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 48.1 (13.5) |
subtype1 | 28 | 46.9 (14.8) |
subtype2 | 29 | 49.2 (14.4) |
subtype3 | 20 | 48.1 (10.1) |
Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.931 (Fisher's exact test), Q value = 1
Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 49 | 25 |
subtype1 | 18 | 8 |
subtype2 | 18 | 10 |
subtype3 | 13 | 7 |
Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.802 (Fisher's exact test), Q value = 1
Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 49 | 24 |
subtype1 | 17 | 9 |
subtype2 | 18 | 10 |
subtype3 | 14 | 5 |
Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.15 (Chi-square test), Q value = 1
Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 48 | 2 | 23 |
subtype1 | 19 | 0 | 6 |
subtype2 | 18 | 0 | 10 |
subtype3 | 11 | 2 | 7 |
Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 0.00809 (Chi-square test), Q value = 0.52
Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 67 | 1 | 8 | 1 | 1 |
subtype1 | 29 | 0 | 0 | 0 | 0 |
subtype2 | 26 | 1 | 2 | 0 | 0 |
subtype3 | 12 | 0 | 6 | 1 | 1 |
Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.297 (Fisher's exact test), Q value = 1
Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 17 | 61 |
subtype1 | 9 | 20 |
subtype2 | 4 | 25 |
subtype3 | 4 | 16 |
Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.871 (ANOVA), Q value = 1
Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 8 | 19.1 (13.8) |
subtype2 | 8 | 20.6 (11.8) |
subtype3 | 3 | 15.8 (17.0) |
Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.602 (ANOVA), Q value = 1
Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 66 | 1.0 (2.4) |
subtype1 | 24 | 1.2 (3.3) |
subtype2 | 25 | 1.1 (2.1) |
subtype3 | 17 | 0.5 (1.0) |
Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S51. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 32 | 12 | 14 | 20 |
P value = 0.208 (logrank test), Q value = 1
Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 76 | 14 | 0.1 - 177.0 (10.1) |
subtype1 | 32 | 6 | 0.3 - 177.0 (20.6) |
subtype2 | 10 | 1 | 1.0 - 95.1 (20.3) |
subtype3 | 14 | 2 | 0.3 - 124.3 (11.2) |
subtype4 | 20 | 5 | 0.1 - 78.7 (2.2) |
Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

P value = 0.202 (ANOVA), Q value = 1
Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 48.1 (13.5) |
subtype1 | 32 | 50.7 (16.1) |
subtype2 | 11 | 50.5 (6.7) |
subtype3 | 14 | 47.9 (11.0) |
subtype4 | 20 | 42.8 (12.3) |
Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

P value = 0.195 (Fisher's exact test), Q value = 1
Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 49 | 25 |
subtype1 | 24 | 7 |
subtype2 | 6 | 5 |
subtype3 | 10 | 4 |
subtype4 | 9 | 9 |
Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.825 (Fisher's exact test), Q value = 1
Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 49 | 24 |
subtype1 | 21 | 10 |
subtype2 | 7 | 4 |
subtype3 | 10 | 3 |
subtype4 | 11 | 7 |
Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.424 (Chi-square test), Q value = 1
Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 48 | 2 | 23 |
subtype1 | 22 | 0 | 9 |
subtype2 | 8 | 1 | 2 |
subtype3 | 7 | 1 | 6 |
subtype4 | 11 | 0 | 6 |
Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 4.21e-06 (Chi-square test), Q value = 3e-04
Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 67 | 1 | 8 | 1 | 1 |
subtype1 | 32 | 0 | 0 | 0 | 0 |
subtype2 | 11 | 0 | 1 | 0 | 0 |
subtype3 | 4 | 1 | 7 | 1 | 1 |
subtype4 | 20 | 0 | 0 | 0 | 0 |
Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.0816 (Fisher's exact test), Q value = 1
Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 17 | 61 |
subtype1 | 9 | 23 |
subtype2 | 5 | 7 |
subtype3 | 1 | 13 |
subtype4 | 2 | 18 |
Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.496 (ANOVA), Q value = 1
Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 8 | 15.9 (8.5) |
subtype2 | 1 | 10.0 (NA) |
subtype3 | 3 | 25.8 (20.4) |
subtype4 | 7 | 21.6 (14.5) |
Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.514 (ANOVA), Q value = 1
Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 66 | 1.0 (2.4) |
subtype1 | 28 | 0.8 (1.8) |
subtype2 | 10 | 2.0 (5.0) |
subtype3 | 12 | 0.5 (1.2) |
subtype4 | 16 | 1.0 (1.8) |
Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S61. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 28 | 30 | 20 |
P value = 0.455 (logrank test), Q value = 1
Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 76 | 14 | 0.1 - 177.0 (10.1) |
subtype1 | 27 | 7 | 0.1 - 177.0 (20.4) |
subtype2 | 29 | 5 | 0.3 - 147.3 (6.8) |
subtype3 | 20 | 2 | 0.1 - 124.3 (9.4) |
Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.6 (ANOVA), Q value = 1
Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 48.1 (13.5) |
subtype1 | 27 | 47.3 (12.8) |
subtype2 | 30 | 50.0 (15.1) |
subtype3 | 20 | 46.4 (12.0) |
Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.396 (Fisher's exact test), Q value = 1
Table S64. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 49 | 25 |
subtype1 | 19 | 6 |
subtype2 | 17 | 12 |
subtype3 | 13 | 7 |
Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.635 (Fisher's exact test), Q value = 1
Table S65. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 49 | 24 |
subtype1 | 15 | 10 |
subtype2 | 20 | 9 |
subtype3 | 14 | 5 |
Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.122 (Chi-square test), Q value = 1
Table S66. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 48 | 2 | 23 |
subtype1 | 19 | 0 | 6 |
subtype2 | 19 | 0 | 10 |
subtype3 | 10 | 2 | 7 |
Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 0.0039 (Chi-square test), Q value = 0.26
Table S67. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 67 | 1 | 8 | 1 | 1 |
subtype1 | 28 | 0 | 0 | 0 | 0 |
subtype2 | 28 | 0 | 2 | 0 | 0 |
subtype3 | 11 | 1 | 6 | 1 | 1 |
Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.113 (Fisher's exact test), Q value = 1
Table S68. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 17 | 61 |
subtype1 | 10 | 18 |
subtype2 | 4 | 26 |
subtype3 | 3 | 17 |
Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.939 (ANOVA), Q value = 1
Table S69. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 7 | 19.7 (14.8) |
subtype2 | 7 | 17.9 (9.5) |
subtype3 | 5 | 20.5 (16.2) |
Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.551 (ANOVA), Q value = 1
Table S70. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 66 | 1.0 (2.4) |
subtype1 | 23 | 1.3 (3.4) |
subtype2 | 27 | 1.0 (2.1) |
subtype3 | 16 | 0.4 (1.0) |
Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Table S71. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 17 | 40 | 21 |
P value = 0.704 (logrank test), Q value = 1
Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 76 | 14 | 0.1 - 177.0 (10.1) |
subtype1 | 16 | 1 | 0.1 - 78.3 (2.2) |
subtype2 | 39 | 10 | 0.3 - 177.0 (14.9) |
subtype3 | 21 | 3 | 0.1 - 124.3 (7.9) |
Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.957 (ANOVA), Q value = 1
Table S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 77 | 48.1 (13.5) |
subtype1 | 16 | 48.9 (11.8) |
subtype2 | 40 | 48.0 (15.7) |
subtype3 | 21 | 47.6 (10.1) |
Figure S65. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.575 (Fisher's exact test), Q value = 1
Table S74. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2+T3+T4 |
---|---|---|
ALL | 49 | 25 |
subtype1 | 12 | 5 |
subtype2 | 25 | 11 |
subtype3 | 12 | 9 |
Figure S66. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

P value = 0.319 (Fisher's exact test), Q value = 1
Table S75. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 49 | 24 |
subtype1 | 14 | 3 |
subtype2 | 22 | 14 |
subtype3 | 13 | 7 |
Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

P value = 0.183 (Chi-square test), Q value = 1
Table S76. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 48 | 2 | 23 |
subtype1 | 11 | 0 | 5 |
subtype2 | 26 | 0 | 10 |
subtype3 | 11 | 2 | 8 |
Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

P value = 2.96e-05 (Chi-square test), Q value = 0.0021
Table S77. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'
nPatients | CERVICAL SQUAMOUS CELL CARCINOMA | ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | ENDOCERVICAL TYPE OF ADENOCARCINOMA | ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE |
---|---|---|---|---|---|
ALL | 67 | 1 | 8 | 1 | 1 |
subtype1 | 17 | 0 | 0 | 0 | 0 |
subtype2 | 40 | 0 | 0 | 0 | 0 |
subtype3 | 10 | 1 | 8 | 1 | 1 |
Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

P value = 0.406 (Fisher's exact test), Q value = 1
Table S78. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 17 | 61 |
subtype1 | 2 | 15 |
subtype2 | 11 | 29 |
subtype3 | 4 | 17 |
Figure S70. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.607 (ANOVA), Q value = 1
Table S79. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 19.2 (12.8) |
subtype1 | 3 | 23.3 (23.6) |
subtype2 | 11 | 16.6 (7.9) |
subtype3 | 5 | 22.5 (16.0) |
Figure S71. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

P value = 0.478 (ANOVA), Q value = 1
Table S80. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 66 | 1.0 (2.4) |
subtype1 | 15 | 0.3 (0.7) |
subtype2 | 33 | 1.1 (2.0) |
subtype3 | 18 | 1.3 (3.8) |
Figure S72. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

-
Cluster data file = CESC-TP.mergedcluster.txt
-
Clinical data file = CESC-TP.merged_data.txt
-
Number of patients = 84
-
Number of clustering approaches = 8
-
Number of selected clinical features = 9
-
Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R
For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
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.