Correlate_Clinical_vs_Molecular_Signatures

Cervical Squamous Cell Carcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1HT2MGG

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 6 different clustering approaches and 11 clinical features across 40 patients, one significant finding 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.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 6 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.458 (1.00)	0.319 (1.00)	0.915 (1.00)	0.802 (1.00)	0.821 (1.00)	0.954 (1.00)
AGE	ANOVA	0.737 (1.00)	0.743 (1.00)	0.322 (1.00)	0.264 (1.00)	0.302 (1.00)	0.215 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.408 (1.00)	0.0793 (1.00)	0.142 (1.00)	0.27 (1.00)	0.669 (1.00)	0.113 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.535 (1.00)	0.402 (1.00)	0.00207 (0.112)	0.458 (1.00)	0.283 (1.00)	0.462 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.636 (1.00)	0.301 (1.00)	0.277 (1.00)	0.232 (1.00)	0.72 (1.00)	0.232 (1.00)
STOPPEDSMOKINGYEAR	ANOVA
TOBACCOSMOKINGHISTORYINDICATOR	ANOVA	0.871 (1.00)	0.933 (1.00)	0.828 (1.00)	0.175 (1.00)	0.69 (1.00)	0.594 (1.00)
DISTANT METASTASIS	Fisher's exact test	0.651 (1.00)	0.0696 (1.00)	0.0474 (1.00)	0.0563 (1.00)	0.382 (1.00)	0.209 (1.00)
LYMPH NODE METASTASIS	Fisher's exact test	0.199 (1.00)	0.811 (1.00)	0.788 (1.00)	0.651 (1.00)	1 (1.00)	0.569 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.385 (1.00)	0.348 (1.00)	0.11 (1.00)	0.776 (1.00)	0.666 (1.00)	0.554 (1.00)
TUMOR STAGECODE	ANOVA

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

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

Cluster Labels	1	2	3
Number of samples	14	6	14

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

P value = 0.458 (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	33	6	0.1 - 177.0 (5.8)
subtype1	13	1	0.3 - 78.3 (5.8)
subtype2	6	3	1.4 - 70.8 (38.3)
subtype3	14	2	0.1 - 177.0 (4.0)

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 = 0.737 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	34	47.8 (12.9)
subtype1	14	47.5 (11.0)
subtype2	6	44.5 (9.9)
subtype3	14	49.5 (16.0)

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 'HISTOLOGICAL.TYPE'

P value = 0.408 (Chi-square test), Q value = 1

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	27	1	6
subtype1	13	0	1
subtype2	5	0	1
subtype3	9	1	4

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.535 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	24
subtype1	4	10
subtype2	3	3
subtype3	3	11

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.636 (ANOVA), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	3	14.0 (5.3)
subtype2	3	23.7 (12.1)
subtype3	4	18.8 (14.9)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.871 (ANOVA), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	33	1.8 (1.1)
subtype1	13	1.8 (1.2)
subtype2	6	1.7 (0.5)
subtype3	14	1.9 (1.2)

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.651 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	20	9
subtype1	10	3
subtype2	3	1
subtype3	7	5

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.199 (Fisher's exact test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	19	11
subtype1	10	3
subtype2	1	3
subtype3	8	5

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.385 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	30	0.5 (0.9)
subtype1	13	0.5 (1.1)
subtype2	4	1.0 (0.8)
subtype3	13	0.3 (0.5)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5
Number of samples	8	6	12	10	4

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.319 (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	39	9	0.1 - 177.0 (6.9)
subtype1	8	1	2.4 - 177.0 (45.9)
subtype2	6	1	1.0 - 95.1 (21.4)
subtype3	11	1	0.6 - 36.8 (2.7)
subtype4	10	5	0.1 - 69.9 (18.5)
subtype5	4	1	1.2 - 53.1 (18.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.743 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	40	49.0 (13.5)
subtype1	8	50.8 (14.9)
subtype2	6	43.7 (13.8)
subtype3	12	48.8 (12.8)
subtype4	10	52.6 (14.7)
subtype5	4	45.5 (12.0)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0793 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	33	1	6
subtype1	7	0	1
subtype2	6	0	0
subtype3	8	0	4
subtype4	9	0	1
subtype5	3	1	0

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.402 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	26
subtype1	2	6
subtype2	4	2
subtype3	3	9
subtype4	3	7
subtype5	2	2

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.301 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	2	17.5 (3.5)
subtype2	1	11.0 (NA)
subtype3	1	12.0 (NA)
subtype4	3	15.0 (10.0)
subtype5	3	28.3 (16.1)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.933 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	37	1.9 (1.1)
subtype1	7	2.0 (1.4)
subtype2	6	1.8 (1.2)
subtype3	11	2.0 (1.2)
subtype4	9	1.7 (1.0)
subtype5	4	2.2 (1.3)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.0696 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	26	9
subtype1	6	1
subtype2	5	0
subtype3	7	4
subtype4	7	1
subtype5	1	3

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.811 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	22	14
subtype1	5	2
subtype2	4	1
subtype3	6	5
subtype4	5	4
subtype5	2	2

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.348 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	36	1.1 (3.0)
subtype1	7	0.1 (0.4)
subtype2	5	0.4 (0.9)
subtype3	11	0.8 (1.3)
subtype4	9	2.9 (5.6)
subtype5	4	0.5 (0.6)

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	19	8	9

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.915 (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	35	7	0.1 - 177.0 (6.0)
subtype1	18	3	0.1 - 177.0 (2.5)
subtype2	8	3	5.5 - 95.1 (35.7)
subtype3	9	1	1.0 - 101.8 (5.8)

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 = 0.322 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	36	47.6 (12.0)
subtype1	19	48.7 (13.1)
subtype2	8	42.0 (9.1)
subtype3	9	50.1 (11.3)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.142 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	31	1	4
subtype1	15	0	4
subtype2	8	0	0
subtype3	8	1	0

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.00207 (Fisher's exact test), Q value = 0.11

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	12	24
subtype1	2	17
subtype2	6	2
subtype3	4	5

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.277 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	4	13.8 (6.3)
subtype2	2	18.0 (9.9)
subtype3	4	24.2 (15.5)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.828 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	33	1.9 (1.1)
subtype1	17	1.8 (1.2)
subtype2	7	1.9 (1.1)
subtype3	9	2.1 (1.2)

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0474 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	23	8
subtype1	13	3
subtype2	6	0
subtype3	4	5

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.788 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	20	12
subtype1	11	6
subtype2	3	3
subtype3	6	3

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.11 (ANOVA), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	32	1.0 (2.9)
subtype1	17	0.4 (0.6)
subtype2	6	3.2 (6.3)
subtype3	9	0.7 (1.3)

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	6	7	23

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.802 (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	35	7	0.1 - 177.0 (6.0)
subtype1	6	2	0.1 - 95.1 (23.2)
subtype2	7	2	1.2 - 101.8 (35.6)
subtype3	22	3	0.3 - 177.0 (5.9)

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 = 0.264 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	36	47.6 (12.0)
subtype1	6	40.3 (10.4)
subtype2	7	48.0 (10.1)
subtype3	23	49.3 (12.6)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.27 (Chi-square test), Q value = 1

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	31	1	4
subtype1	5	0	1
subtype2	6	1	0
subtype3	20	0	3

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.458 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	12	24
subtype1	3	3
subtype2	3	4
subtype3	6	17

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.232 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	4	14.0 (8.4)
subtype2	3	28.3 (16.1)
subtype3	3	15.7 (4.0)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.175 (ANOVA), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	33	1.9 (1.1)
subtype1	6	2.0 (1.1)
subtype2	7	2.6 (1.4)
subtype3	20	1.6 (1.0)

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0563 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	23	8
subtype1	2	1
subtype2	3	4
subtype3	18	3

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.651 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	20	12
subtype1	2	2
subtype2	4	3
subtype3	14	7

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.776 (ANOVA), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	32	1.0 (2.9)
subtype1	4	0.5 (0.6)
subtype2	7	0.4 (0.5)
subtype3	21	1.2 (3.5)

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	15	15	10

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.821 (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	39	9	0.1 - 177.0 (6.9)
subtype1	15	5	0.1 - 177.0 (12.4)
subtype2	14	3	0.6 - 101.8 (15.5)
subtype3	10	1	1.0 - 78.3 (4.3)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.302 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	40	49.0 (13.5)
subtype1	15	47.0 (15.1)
subtype2	15	53.3 (11.5)
subtype3	10	45.7 (13.2)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.669 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	33	1	6
subtype1	13	0	2
subtype2	11	1	3
subtype3	9	0	1

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.283 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	26
subtype1	6	9
subtype2	3	12
subtype3	5	5

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.72 (ANOVA), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	4	15.2 (9.0)
subtype2	2	27.5 (17.7)
subtype3	4	18.0 (11.5)

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

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.69 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	37	1.9 (1.1)
subtype1	14	1.7 (1.1)
subtype2	14	2.0 (1.2)
subtype3	9	2.1 (1.3)

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.382 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	26	9
subtype1	10	2
subtype2	11	3
subtype3	5	4

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

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

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	22	14
subtype1	7	5
subtype2	9	5
subtype3	6	4

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.666 (ANOVA), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	36	1.1 (3.0)
subtype1	12	1.8 (4.5)
subtype2	14	0.9 (2.1)
subtype3	10	0.7 (1.3)

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3
Number of samples	5	22	13

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.954 (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	39	9	0.1 - 177.0 (6.9)
subtype1	5	1	1.2 - 53.1 (6.0)
subtype2	22	6	0.3 - 177.0 (17.1)
subtype3	12	2	0.1 - 95.1 (6.2)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.215 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	40	49.0 (13.5)
subtype1	5	45.6 (10.4)
subtype2	22	52.4 (14.8)
subtype3	13	44.6 (11.0)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.113 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL TYPE OF ADENOCARCINOMA	SQUAMOUS CELL CARCINOMA
ALL	33	1	6
subtype1	3	1	1
subtype2	19	0	3
subtype3	11	0	2

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.462 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	14	26
subtype1	3	2
subtype2	7	15
subtype3	4	9

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.232 (ANOVA), Q value = 1

Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	18.8 (11.3)
subtype1	3	28.3 (16.1)
subtype2	3	15.7 (4.0)
subtype3	4	14.0 (8.4)

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

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.594 (ANOVA), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	37	1.9 (1.1)
subtype1	5	2.4 (1.1)
subtype2	21	1.8 (1.2)
subtype3	11	1.9 (1.1)

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.209 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	26	9
subtype1	2	3
subtype2	17	4
subtype3	7	2

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.569 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	22	14
subtype1	2	3
subtype2	13	8
subtype3	7	3

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.554 (ANOVA), Q value = 1

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	36	1.1 (3.0)
subtype1	5	0.6 (0.5)
subtype2	21	1.6 (3.8)
subtype3	10	0.4 (0.7)

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: '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 = 40
Number of clustering approaches = 6
Number of selected clinical features = 11
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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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

[6] 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)

[7] 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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