Correlation between aggregated molecular cancer subtypes and selected clinical features

Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C11G0JJ0

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 9 clinical features across 74 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 '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'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
2 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 correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

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	0.147 (1.00)	0.566 (1.00)	0.166 (1.00)	0.694 (1.00)	0.61 (1.00)	0.861 (1.00)	0.74 (1.00)	0.942 (1.00)
AGE	ANOVA	0.0617 (1.00)	0.0778 (1.00)	0.521 (1.00)	0.701 (1.00)	0.108 (1.00)	0.864 (1.00)	0.177 (1.00)	0.297 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.414 (1.00)	0.688 (1.00)	0.578 (1.00)	0.352 (1.00)	0.651 (1.00)	0.537 (1.00)	0.65 (1.00)	0.939 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.62 (1.00)	0.86 (1.00)	0.695 (1.00)	0.728 (1.00)	0.604 (1.00)	0.532 (1.00)	1 (1.00)	0.507 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.384 (1.00)	0.0322 (1.00)	0.758 (1.00)	0.0834 (1.00)	0.179 (1.00)	0.0283 (1.00)	0.113 (1.00)	0.178 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.257 (1.00)	7.36e-05 (0.00523)	0.0364 (1.00)	0.00015 (0.0101)	0.000122 (0.00827)	1.13e-06 (8.15e-05)	9.65e-05 (0.00675)	9.65e-05 (0.00675)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.55 (1.00)	0.353 (1.00)	0.0223 (1.00)	0.711 (1.00)	0.253 (1.00)	1 (1.00)	0.133 (1.00)	1 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.421 (1.00)	0.96 (1.00)	0.872 (1.00)	0.714 (1.00)	0.838 (1.00)	0.707 (1.00)	0.728 (1.00)	0.654 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.494 (1.00)	0.787 (1.00)	0.645 (1.00)	0.705 (1.00)	0.546 (1.00)	0.369 (1.00)	0.77 (1.00)	0.321 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	24	16	28

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.147 (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	66	10	0.1 - 177.0 (6.4)
subtype1	22	2	0.3 - 117.4 (7.0)
subtype2	16	6	0.1 - 147.3 (23.5)
subtype3	28	2	0.1 - 177.0 (2.3)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0617 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	67	48.2 (13.1)
subtype1	23	47.3 (9.9)
subtype2	16	42.6 (11.3)
subtype3	28	52.1 (15.3)

Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.414 (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	42	22
subtype1	13	10
subtype2	11	3
subtype3	18	9

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.62 (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	42	21
subtype1	15	8
subtype2	8	6
subtype3	19	7

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.384 (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	40	2	21
subtype1	17	1	5
subtype2	10	0	4
subtype3	13	1	12

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.257 (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
ALL	58	1	8	1
subtype1	23	0	1	0
subtype2	15	0	1	0
subtype3	20	1	6	1

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.55 (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	14	54
subtype1	4	20
subtype2	5	11
subtype3	5	23

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.421 (ANOVA), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	19	19.2 (12.8)
subtype1	4	13.0 (4.8)
subtype2	6	17.7 (11.2)
subtype3	9	23.1 (15.6)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.494 (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	57	0.7 (1.3)
subtype1	20	0.8 (1.3)
subtype2	14	0.9 (1.4)
subtype3	23	0.4 (1.3)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	14	12	9	8	16	9	6

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.566 (logrank test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	13	0.1 - 177.0 (7.4)
subtype1	14	2	0.1 - 177.0 (14.8)
subtype2	11	2	0.3 - 124.3 (6.0)
subtype3	9	0	0.3 - 78.3 (4.3)
subtype4	8	2	0.1 - 95.1 (6.2)
subtype5	15	6	0.1 - 147.3 (26.6)
subtype6	9	1	1.1 - 96.9 (7.9)
subtype7	6	0	0.6 - 53.1 (4.8)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0778 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	73	48.8 (13.4)
subtype1	14	47.1 (12.7)
subtype2	12	51.9 (6.7)
subtype3	9	46.3 (12.8)
subtype4	8	38.5 (10.6)
subtype5	15	50.9 (14.4)
subtype6	9	47.6 (12.0)
subtype7	6	60.7 (20.7)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.688 (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	46	24
subtype1	9	4
subtype2	6	5
subtype3	5	4
subtype4	4	3
subtype5	12	3
subtype6	5	4
subtype7	5	1

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.86 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	45	24
subtype1	7	6
subtype2	7	4
subtype3	5	4
subtype4	5	2
subtype5	11	4
subtype6	5	3
subtype7	5	1

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.0322 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	46	2	21
subtype1	8	0	5
subtype2	8	0	3
subtype3	5	0	4
subtype4	6	0	1
subtype5	13	0	1
subtype6	3	2	4
subtype7	3	0	3

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 7.36e-05 (Chi-square test), Q value = 0.0052

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
ALL	64	1	8	1
subtype1	14	0	0	0
subtype2	9	0	2	1
subtype3	9	0	0	0
subtype4	8	0	0	0
subtype5	16	0	0	0
subtype6	2	1	6	0
subtype7	6	0	0	0

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.353 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	18	56
subtype1	5	9
subtype2	4	8
subtype3	2	7
subtype4	3	5
subtype5	2	14
subtype6	0	9
subtype7	2	4

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.96 (ANOVA), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	19	19.2 (12.8)
subtype1	5	14.0 (4.2)
subtype2	2	23.5 (16.3)
subtype3	2	37.5 (17.7)
subtype4	2	15.5 (6.4)
subtype5	4	13.8 (8.5)
subtype6	2	21.2 (26.5)
subtype7	2	22.5 (17.7)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.787 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	63	1.0 (2.5)
subtype1	9	0.8 (1.4)
subtype2	10	2.2 (5.0)
subtype3	9	1.1 (1.5)
subtype4	7	1.1 (2.3)
subtype5	14	0.8 (2.1)
subtype6	8	0.8 (1.4)
subtype7	6	0.2 (0.4)

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	21	14	7	9	12

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.166 (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	62	11	0.1 - 177.0 (8.0)
subtype1	21	3	0.1 - 177.0 (2.7)
subtype2	13	3	0.3 - 36.8 (1.5)
subtype3	7	0	0.1 - 101.8 (2.7)
subtype4	9	4	5.5 - 95.1 (40.9)
subtype5	12	1	1.2 - 147.3 (41.4)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.521 (ANOVA), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	63	48.4 (13.1)
subtype1	21	48.9 (16.0)
subtype2	14	52.1 (9.8)
subtype3	7	50.1 (7.8)
subtype4	9	42.4 (14.8)
subtype5	12	46.8 (11.8)

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.578 (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	40	19
subtype1	14	6
subtype2	7	6
subtype3	4	3
subtype4	6	1
subtype5	9	3

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.695 (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	39	20
subtype1	13	7
subtype2	8	5
subtype3	4	3
subtype4	4	3
subtype5	10	2

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.758 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	38	20
subtype1	12	7
subtype2	9	4
subtype3	4	3
subtype4	6	1
subtype5	7	5

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0364 (Chi-square test), Q value = 1

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
ALL	55	1	6	1
subtype1	21	0	0	0
subtype2	12	0	2	0
subtype3	7	0	0	0
subtype4	9	0	0	0
subtype5	6	1	4	1

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0223 (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	47
subtype1	3	18
subtype2	4	10
subtype3	2	5
subtype4	6	3
subtype5	1	11

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.645 (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	56	1.0 (2.4)
subtype1	19	0.9 (1.8)
subtype2	12	1.9 (4.6)
subtype3	6	0.7 (1.0)
subtype4	7	0.7 (1.0)
subtype5	12	0.4 (1.2)

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	16	38	9

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.694 (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	62	11	0.1 - 177.0 (8.0)
subtype1	16	3	0.3 - 124.3 (9.7)
subtype2	38	8	0.1 - 177.0 (17.0)
subtype3	8	0	0.1 - 101.8 (1.5)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.701 (ANOVA), Q value = 1

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	63	48.4 (13.1)
subtype1	16	48.7 (12.3)
subtype2	38	47.6 (13.8)
subtype3	9	51.7 (11.8)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.352 (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	40	19
subtype1	10	6
subtype2	26	9
subtype3	4	4

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.728 (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	39	20
subtype1	12	4
subtype2	22	13
subtype3	5	3

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0834 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	38	20
subtype1	9	7
subtype2	26	8
subtype3	3	5

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00015 (Chi-square test), Q value = 0.01

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
ALL	55	1	6	1
subtype1	8	1	6	1
subtype2	38	0	0	0
subtype3	9	0	0	0

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.711 (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	47
subtype1	3	13
subtype2	10	28
subtype3	3	6

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.714 (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	5	21.5 (16.0)
subtype2	12	18.4 (12.8)
subtype3	2	18.5 (9.2)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.705 (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	56	1.0 (2.4)
subtype1	15	0.5 (1.1)
subtype2	34	1.2 (3.0)
subtype3	7	1.0 (1.5)

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	26	19	6	14

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.61 (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	63	13	0.1 - 177.0 (11.4)
subtype1	25	5	0.3 - 177.0 (21.7)
subtype2	18	5	0.1 - 95.1 (9.2)
subtype3	6	1	0.1 - 78.7 (2.5)
subtype4	14	2	0.7 - 124.3 (21.8)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.108 (ANOVA), Q value = 1

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	48.2 (13.2)
subtype1	26	50.2 (12.7)
subtype2	18	42.9 (11.6)
subtype3	6	56.7 (19.6)
subtype4	14	47.8 (11.4)

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.651 (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	41	20
subtype1	16	8
subtype2	13	4
subtype3	3	3
subtype4	9	5

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.604 (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	39	21
subtype1	16	8
subtype2	9	8
subtype3	5	1
subtype4	9	4

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.179 (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	42	2	16
subtype1	17	0	7
subtype2	13	0	3
subtype3	5	0	1
subtype4	7	2	5

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000122 (Chi-square test), Q value = 0.0083

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
ALL	57	1	6	1
subtype1	26	0	0	0
subtype2	19	0	0	0
subtype3	6	0	0	0
subtype4	6	1	6	1

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.253 (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	18	47
subtype1	7	19
subtype2	8	11
subtype3	0	6
subtype4	3	11

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.838 (ANOVA), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	17	18.9 (12.8)
subtype1	5	16.4 (6.1)
subtype2	6	20.2 (16.0)
subtype3	2	15.0 (7.1)
subtype4	4	21.9 (18.4)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.546 (ANOVA), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	55	1.0 (2.5)
subtype1	23	0.9 (2.0)
subtype2	15	1.7 (4.0)
subtype3	5	0.0 (0.0)
subtype4	12	0.6 (1.2)

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2
Number of samples	50	15

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.861 (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	63	13	0.1 - 177.0 (11.4)
subtype1	48	10	0.1 - 177.0 (11.9)
subtype2	15	3	0.7 - 124.3 (7.9)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.864 (t-test), Q value = 1

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	48.2 (13.2)
subtype1	49	48.1 (14.1)
subtype2	15	48.7 (9.9)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.537 (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	41	20
subtype1	32	14
subtype2	9	6

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.532 (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	39	21
subtype1	31	15
subtype2	8	6

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0283 (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	42	2	16
subtype1	34	0	11
subtype2	8	2	5

Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1.13e-06 (Chi-square test), Q value = 8.1e-05

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
ALL	57	1	6	1
subtype1	50	0	0	0
subtype2	7	1	6	1

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (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	18	47
subtype1	14	36
subtype2	4	11

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.707 (t-test), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	17	18.9 (12.8)
subtype1	13	17.9 (11.3)
subtype2	4	21.9 (18.4)

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.369 (t-test), Q value = 1

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	55	1.0 (2.5)
subtype1	42	0.7 (1.5)
subtype2	13	1.8 (4.4)

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	27	22	16

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.74 (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	63	13	0.1 - 177.0 (11.4)
subtype1	26	6	0.1 - 177.0 (19.5)
subtype2	21	5	0.6 - 147.3 (16.4)
subtype3	16	2	0.1 - 124.3 (2.0)

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.177 (ANOVA), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	48.2 (13.2)
subtype1	26	45.7 (13.4)
subtype2	22	52.5 (13.8)
subtype3	16	46.6 (11.3)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.65 (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	41	20
subtype1	18	6
subtype2	13	8
subtype3	10	6

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 1 (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	39	21
subtype1	15	9
subtype2	14	7
subtype3	10	5

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.113 (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	42	2	16
subtype1	19	0	5
subtype2	15	0	6
subtype3	8	2	5

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.65e-05 (Chi-square test), Q value = 0.0068

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
ALL	57	1	6	1
subtype1	27	0	0	0
subtype2	22	0	0	0
subtype3	8	1	6	1

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.133 (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	18	47
subtype1	11	16
subtype2	5	17
subtype3	2	14

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.728 (ANOVA), Q value = 1

Table S69. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	17	18.9 (12.8)
subtype1	6	21.0 (15.8)
subtype2	6	15.3 (6.1)
subtype3	5	20.5 (16.2)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.77 (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	55	1.0 (2.5)
subtype1	22	1.2 (3.4)
subtype2	20	1.0 (2.1)
subtype3	13	0.5 (1.1)

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	29	20	16

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.942 (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	63	13	0.1 - 177.0 (11.4)
subtype1	28	6	0.3 - 177.0 (18.6)
subtype2	19	4	0.1 - 101.8 (6.9)
subtype3	16	3	0.1 - 124.3 (16.7)

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.297 (ANOVA), Q value = 1

Table S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	48.2 (13.2)
subtype1	29	51.1 (15.2)
subtype2	19	45.6 (12.3)
subtype3	16	46.2 (9.5)

Figure S65. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.939 (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	41	20
subtype1	19	8
subtype2	12	6
subtype3	10	6

Figure S66. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.507 (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	39	21
subtype1	18	9
subtype2	13	5
subtype3	8	7

Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.178 (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	42	2	16
subtype1	20	0	7
subtype2	13	0	4
subtype3	9	2	5

Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.65e-05 (Chi-square test), Q value = 0.0068

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
ALL	57	1	6	1
subtype1	29	0	0	0
subtype2	20	0	0	0
subtype3	8	1	6	1

Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (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	18	47
subtype1	8	21
subtype2	6	14
subtype3	4	12

Figure S70. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.654 (ANOVA), Q value = 1

Table S79. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	17	18.9 (12.8)
subtype1	7	15.3 (5.5)
subtype2	6	21.0 (15.8)
subtype3	4	21.9 (18.4)

Figure S71. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.321 (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	55	1.0 (2.5)
subtype1	25	0.8 (1.9)
subtype2	17	0.5 (0.8)
subtype3	13	1.8 (4.4)

Figure S72. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = CESC-TP.mergedcluster.txt
Clinical data file = CESC-TP.clin.merged.picked.txt
Number of patients = 74
Number of clustering approaches = 8
Number of selected clinical features = 9
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Q value calculation

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.

Download Results

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.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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