Correlate_Clinical_vs_Molecular_Signatures

Breast Invasive 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/C1X63K31

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 10 different clustering approaches and 9 clinical features across 869 patients, 8 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'GENDER'.
6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
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 10 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, 8 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	RADIATIONS RADIATION REGIMENINDICATION	DISTANT METASTASIS	LYMPH NODE METASTASIS	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test	Chi-square test	Chi-square test	ANOVA	ANOVA	Chi-square test
mRNA CNMF subtypes	0.23 (1.00)	0.000889 (0.0684)	0.33 (1.00)	0.563 (1.00)	0.104 (1.00)	0.0983 (1.00)	0.338 (1.00)		0.00427 (0.303)
mRNA cHierClus subtypes	0.65 (1.00)	0.000537 (0.0419)	0.323 (1.00)	0.12 (1.00)	0.0773 (1.00)	0.127 (1.00)	0.883 (1.00)		0.0412 (1.00)
Copy Number Ratio CNMF subtypes	0.254 (1.00)	0.0586 (1.00)	0.000114 (0.009)	0.37 (1.00)	0.481 (1.00)	0.044 (1.00)	0.161 (1.00)		0.0488 (1.00)
METHLYATION CNMF	0.237 (1.00)	0.0023 (0.168)	0.0446 (1.00)	0.345 (1.00)	0.753 (1.00)	0.0672 (1.00)	0.144 (1.00)		0.0308 (1.00)
RPPA CNMF subtypes	0.0477 (1.00)	0.0287 (1.00)	0.0564 (1.00)	0.981 (1.00)	0.323 (1.00)	0.0979 (1.00)	0.0305 (1.00)		0.0292 (1.00)
RPPA cHierClus subtypes	0.318 (1.00)	0.00121 (0.0916)	0.154 (1.00)	0.917 (1.00)	0.665 (1.00)	0.194 (1.00)	0.0553 (1.00)		0.0496 (1.00)
RNAseq CNMF subtypes	0.257 (1.00)	0.0125 (0.861)	0.0684 (1.00)	0.331 (1.00)	0.0734 (1.00)	0.214 (1.00)	0.21 (1.00)		0.00203 (0.15)
RNAseq cHierClus subtypes	0.233 (1.00)	0.00187 (0.14)	0.0578 (1.00)	0.1 (1.00)	0.337 (1.00)	0.113 (1.00)	0.385 (1.00)		0.012 (0.84)
MIRSEQ CNMF	0.0226 (1.00)	0.0554 (1.00)	0.327 (1.00)	0.208 (1.00)	0.696 (1.00)	0.00359 (0.258)	0.136 (1.00)		5.55e-05 (0.00444)
MIRSEQ CHIERARCHICAL	0.329 (1.00)	0.0631 (1.00)	0.187 (1.00)	0.385 (1.00)	0.561 (1.00)	0.464 (1.00)	0.813 (1.00)		0.647 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	268	93	166

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.23 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	495	65	0.1 - 223.4 (24.2)
subtype1	257	30	0.1 - 223.4 (20.4)
subtype2	88	10	0.3 - 140.5 (35.6)
subtype3	150	25	0.1 - 211.5 (22.3)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.000889 (ANOVA), Q value = 0.068

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

	nPatients	Mean (Std.Dev)
ALL	527	57.9 (13.3)
subtype1	268	59.9 (13.1)
subtype2	93	54.7 (13.6)
subtype3	166	56.4 (12.9)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.33 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	521	6
subtype1	263	5
subtype2	93	0
subtype3	165	1

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.563 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	148	379
subtype1	70	198
subtype2	27	66
subtype3	51	115

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

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.104 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	497	14	14
subtype1	2	248	7	11
subtype2	0	92	0	1
subtype3	0	157	7	2

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0983 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3C	NX
ALL	138	16	101	59	77	20	2	13	32	29	10	18	1	11
subtype1	67	9	48	27	43	14	1	10	17	11	5	7	0	9
subtype2	24	6	20	7	17	3	1	1	3	6	1	3	0	1
subtype3	47	1	33	25	17	3	0	2	12	12	4	8	1	1

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.338 (ANOVA), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	407	1.8 (3.5)
subtype1	194	1.6 (2.9)
subtype2	86	2.0 (4.5)
subtype3	127	2.1 (3.6)

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00427 (Chi-square test), Q value = 0.3

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	42	41	6	185	111	77	15	19	14	1	16
subtype1	21	26	5	88	56	38	11	5	7	1	10
subtype2	16	6	1	30	18	16	0	3	0	0	3
subtype3	5	9	0	67	37	23	4	11	7	0	3

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	127	264	136

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.65 (logrank test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	495	65	0.1 - 223.4 (24.2)
subtype1	116	15	0.1 - 211.5 (21.9)
subtype2	245	36	0.1 - 223.4 (23.1)
subtype3	134	14	0.1 - 157.4 (26.5)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.000537 (ANOVA), Q value = 0.042

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	527	57.9 (13.3)
subtype1	127	55.0 (12.7)
subtype2	264	60.0 (13.2)
subtype3	136	56.4 (13.4)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.323 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	521	6
subtype1	127	0
subtype2	259	5
subtype3	135	1

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.12 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	148	379
subtype1	44	83
subtype2	65	199
subtype3	39	97

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0773 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	497	14	14
subtype1	0	123	3	1
subtype2	0	245	10	9
subtype3	2	129	1	4

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.127 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3C	NX
ALL	138	16	101	59	77	20	2	13	32	29	10	18	1	11
subtype1	38	3	31	18	10	4	0	1	7	6	3	5	1	0
subtype2	66	4	42	30	45	12	1	10	19	15	4	8	0	8
subtype3	34	9	28	11	22	4	1	2	6	8	3	5	0	3

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.883 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	407	1.8 (3.5)
subtype1	110	1.7 (3.4)
subtype2	178	1.8 (3.2)
subtype3	119	1.9 (4.1)

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0412 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	42	41	6	185	111	77	15	19	14	1	16
subtype1	8	11	0	53	26	13	2	8	3	0	3
subtype2	17	18	3	83	64	43	12	6	10	0	8
subtype3	17	12	3	49	21	21	1	5	1	1	5

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	318	73	196	204	55

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

P value = 0.254 (logrank test), Q value = 1

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	788	94	0.0 - 223.4 (18.2)
subtype1	297	31	0.0 - 223.4 (20.1)
subtype2	72	6	0.0 - 189.0 (19.0)
subtype3	179	21	0.1 - 162.0 (16.5)
subtype4	189	25	0.0 - 211.5 (17.7)
subtype5	51	11	0.7 - 220.9 (21.5)

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0586 (ANOVA), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	845	58.5 (13.2)
subtype1	317	58.1 (12.9)
subtype2	73	61.0 (11.3)
subtype3	196	59.0 (14.6)
subtype4	204	57.0 (13.0)
subtype5	55	61.7 (12.3)

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.000114 (Chi-square test), Q value = 0.009

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	837	9
subtype1	318	0
subtype2	72	1
subtype3	188	8
subtype4	204	0
subtype5	55	0

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.37 (Chi-square test), Q value = 1

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	204	642
subtype1	67	251
subtype2	19	54
subtype3	45	151
subtype4	57	147
subtype5	16	39

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.481 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	753	15	76
subtype1	1	281	4	32
subtype2	0	67	2	4
subtype3	0	174	5	17
subtype4	0	185	3	16
subtype5	1	46	1	7

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.044 (Chi-square test), Q value = 1

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	N3C	NX
ALL	245	22	127	1	101	124	32	2	22	51	53	19	30	2	1	14
subtype1	87	12	56	1	37	50	11	2	10	14	14	7	10	1	0	6
subtype2	23	2	9	0	4	19	3	0	0	1	4	3	4	0	0	1
subtype3	48	1	21	0	29	24	7	0	8	18	19	7	8	0	0	6
subtype4	74	5	35	0	24	21	6	0	3	16	10	2	6	1	1	0
subtype5	13	2	6	0	7	10	5	0	1	2	6	0	2	0	0	1

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.161 (ANOVA), Q value = 1

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	704	2.3 (4.4)
subtype1	279	2.0 (4.2)
subtype2	57	2.8 (5.4)
subtype3	145	2.9 (4.4)
subtype4	176	1.9 (4.5)
subtype5	47	2.8 (4.5)

Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0488 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	71	62	7	8	287	186	2	124	25	41	15	1	16
subtype1	39	35	3	5	95	70	1	39	5	15	4	0	7
subtype2	3	2	0	1	26	19	0	8	5	4	2	0	3
subtype3	11	9	2	1	61	43	1	39	12	11	5	0	1
subtype4	15	12	2	1	83	44	0	27	3	9	3	1	3
subtype5	3	4	0	0	22	10	0	11	0	2	1	0	2

Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S28. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6
Number of samples	96	139	80	125	33	82

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.237 (logrank test), Q value = 1

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	526	61	0.0 - 223.4 (17.8)
subtype1	91	14	0.2 - 211.5 (19.2)
subtype2	131	13	0.3 - 223.4 (13.6)
subtype3	78	12	0.0 - 109.9 (16.1)
subtype4	118	12	0.1 - 194.3 (19.0)
subtype5	32	3	0.1 - 157.4 (20.4)
subtype6	76	7	0.0 - 130.2 (25.6)

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0023 (ANOVA), Q value = 0.17

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	554	57.8 (13.1)
subtype1	96	56.3 (13.1)
subtype2	139	59.7 (12.6)
subtype3	80	62.0 (11.6)
subtype4	125	55.4 (14.4)
subtype5	33	55.7 (13.7)
subtype6	81	56.5 (11.7)

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0446 (Chi-square test), Q value = 1

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	549	6
subtype1	96	0
subtype2	138	1
subtype3	78	2
subtype4	124	1
subtype5	31	2
subtype6	82	0

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.345 (Chi-square test), Q value = 1

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	142	413
subtype1	29	67
subtype2	34	105
subtype3	17	63
subtype4	38	87
subtype5	5	28
subtype6	19	63

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.753 (Chi-square test), Q value = 1

Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	480	6	68
subtype1	0	85	2	9
subtype2	1	119	0	19
subtype3	0	67	2	11
subtype4	0	111	1	13
subtype5	0	28	1	4
subtype6	0	70	0	12

Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0672 (Chi-square test), Q value = 1

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	NX
ALL	150	14	78	1	72	83	23	2	14	38	41	13	17	2	7
subtype1	40	2	15	0	11	10	3	0	0	5	6	1	2	1	0
subtype2	29	3	19	1	12	35	8	1	5	8	8	2	4	0	4
subtype3	24	2	10	0	10	7	3	0	0	9	7	2	4	0	2
subtype4	23	2	16	0	24	20	6	0	5	8	13	2	5	0	1
subtype5	9	1	4	0	3	5	2	0	0	4	1	3	1	0	0
subtype6	25	4	14	0	12	6	1	1	4	4	6	3	1	1	0

Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.144 (ANOVA), Q value = 1

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	515	2.5 (4.6)
subtype1	92	1.8 (3.3)
subtype2	131	2.4 (4.1)
subtype3	74	3.2 (6.3)
subtype4	111	2.7 (4.5)
subtype5	29	3.9 (5.6)
subtype6	78	1.9 (4.7)

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0308 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	47	40	3	8	181	123	2	95	15	29	6	5
subtype1	5	7	1	1	40	21	0	13	1	3	2	1
subtype2	12	13	0	2	37	39	0	24	4	6	0	2
subtype3	6	3	1	2	26	15	1	16	0	6	2	2
subtype4	7	7	1	0	35	33	0	27	8	6	1	0
subtype5	2	5	0	1	10	3	0	5	2	4	1	0
subtype6	15	5	0	2	33	12	1	10	0	4	0	0

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S37. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	152	136	120

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0477 (logrank test), Q value = 1

Table S38. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	380	44	0.1 - 189.0 (24.5)
subtype1	135	22	0.1 - 186.4 (22.8)
subtype2	132	11	0.2 - 146.5 (18.0)
subtype3	113	11	0.3 - 189.0 (28.5)

Figure S33. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0287 (ANOVA), Q value = 1

Table S39. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	152	56.1 (13.4)
subtype2	136	60.1 (13.7)
subtype3	120	57.5 (11.7)

Figure S34. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0564 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	403	5
subtype1	152	0
subtype2	132	4
subtype3	119	1

Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.981 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	125	283
subtype1	47	105
subtype2	42	94
subtype3	36	84

Figure S36. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.323 (Chi-square test), Q value = 1

Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	386	14	7
subtype1	0	143	8	1
subtype2	0	129	3	4
subtype3	1	114	3	2

Figure S37. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0979 (Chi-square test), Q value = 1

Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3C	NX
ALL	100	13	79	48	63	8	1	12	27	25	9	15	1	7
subtype1	41	2	27	24	14	3	0	4	11	11	4	7	1	3
subtype2	31	6	28	10	35	4	1	3	7	3	2	4	0	2
subtype3	28	5	24	14	14	1	0	5	9	11	3	4	0	2

Figure S38. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0305 (ANOVA), Q value = 1

Table S44. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	330	1.9 (3.5)
subtype1	114	2.1 (3.6)
subtype2	113	1.2 (2.0)
subtype3	103	2.4 (4.5)

Figure S39. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0292 (Chi-square test), Q value = 1

Table S45. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	30	31	4	137	94	62	13	15	14	8
subtype1	6	8	0	55	36	24	2	10	8	3
subtype2	11	13	1	46	38	14	6	1	3	3
subtype3	13	10	3	36	20	24	5	4	3	2

Figure S40. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	163	115	130

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.318 (logrank test), Q value = 1

Table S47. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	380	44	0.1 - 189.0 (24.5)
subtype1	152	17	0.2 - 129.7 (24.2)
subtype2	112	10	0.2 - 173.0 (25.5)
subtype3	116	17	0.1 - 189.0 (21.9)

Figure S41. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00121 (ANOVA), Q value = 0.092

Table S48. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	163	60.4 (13.6)
subtype2	115	57.6 (11.8)
subtype3	130	54.8 (13.0)

Figure S42. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.154 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	403	5
subtype1	159	4
subtype2	114	1
subtype3	130	0

Figure S43. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.917 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	125	283
subtype1	48	115
subtype2	36	79
subtype3	41	89

Figure S44. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.665 (Chi-square test), Q value = 1

Table S51. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	386	14	7
subtype1	0	153	6	4
subtype2	1	109	3	2
subtype3	0	124	5	1

Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.194 (Chi-square test), Q value = 1

Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3C	NX
ALL	100	13	79	48	63	8	1	12	27	25	9	15	1	7
subtype1	42	5	31	14	33	3	1	4	13	6	2	5	0	4
subtype2	22	6	23	11	20	1	0	5	7	11	3	4	0	2
subtype3	36	2	25	23	10	4	0	3	7	8	4	6	1	1

Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0553 (ANOVA), Q value = 1

Table S53. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	330	1.9 (3.5)
subtype1	126	1.3 (2.1)
subtype2	99	2.4 (4.5)
subtype3	105	2.1 (3.7)

Figure S47. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0496 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	30	31	4	137	94	62	13	15	14	8
subtype1	14	12	1	52	42	23	7	2	6	4
subtype2	11	12	3	31	21	23	5	4	3	2
subtype3	5	7	0	54	31	16	1	9	5	2

Figure S48. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S55. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	239	156	441

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.257 (logrank test), Q value = 1

Table S56. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	779	94	0.0 - 223.4 (18.8)
subtype1	221	33	0.0 - 211.5 (19.3)
subtype2	150	12	0.3 - 194.3 (25.4)
subtype3	408	49	0.0 - 223.4 (17.0)

Figure S49. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0125 (ANOVA), Q value = 0.86

Table S57. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	835	58.2 (13.2)
subtype1	239	57.2 (12.8)
subtype2	155	56.2 (12.3)
subtype3	441	59.4 (13.5)

Figure S50. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0684 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	827	9
subtype1	239	0
subtype2	155	1
subtype3	433	8

Figure S51. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.331 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	204	632
subtype1	58	181
subtype2	45	111
subtype3	101	340

Figure S52. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0734 (Chi-square test), Q value = 1

Table S60. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	748	15	71
subtype1	0	220	4	15
subtype2	2	136	2	16
subtype3	0	392	9	40

Figure S53. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.214 (Chi-square test), Q value = 1

Table S61. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	N3C	NX
ALL	240	22	129	1	101	124	32	2	22	50	50	17	29	1	1	15
subtype1	75	6	44	0	34	24	8	1	4	14	12	3	8	1	1	4
subtype2	44	8	24	1	15	25	6	0	6	4	8	5	8	0	0	2
subtype3	121	8	61	0	52	75	18	1	12	32	30	9	13	0	0	9

Figure S54. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.21 (ANOVA), Q value = 1

Table S62. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	695	2.2 (4.3)
subtype1	199	2.1 (4.4)
subtype2	145	2.7 (5.9)
subtype3	351	2.0 (3.2)

Figure S55. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00203 (Chi-square test), Q value = 0.15

Table S63. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	69	62	8	8	290	185	2	118	23	37	15	1	17
subtype1	14	16	1	2	101	51	0	30	3	12	4	0	4
subtype2	21	15	2	5	42	34	1	20	0	10	2	1	3
subtype3	34	31	5	1	147	100	1	68	20	15	9	0	10

Figure S56. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	418	219	199

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.233 (logrank test), Q value = 1

Table S65. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	779	94	0.0 - 223.4 (18.8)
subtype1	385	47	0.0 - 223.4 (16.3)
subtype2	211	19	0.1 - 194.3 (25.4)
subtype3	183	28	0.0 - 211.5 (20.0)

Figure S57. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00187 (ANOVA), Q value = 0.14

Table S66. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	835	58.2 (13.2)
subtype1	418	59.8 (13.3)
subtype2	218	56.4 (13.0)
subtype3	199	56.7 (12.8)

Figure S58. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0578 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	827	9
subtype1	410	8
subtype2	218	1
subtype3	199	0

Figure S59. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.1 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	204	632
subtype1	89	329
subtype2	62	157
subtype3	53	146

Figure S60. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.337 (Chi-square test), Q value = 1

Table S69. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	748	15	71
subtype1	1	370	7	40
subtype2	1	191	5	22
subtype3	0	187	3	9

Figure S61. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

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

Table S70. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	N3C	NX
ALL	240	22	129	1	101	124	32	2	22	50	50	17	29	1	1	15
subtype1	118	8	52	1	48	72	17	1	10	31	29	7	14	0	0	10
subtype2	55	9	40	0	26	34	7	0	10	8	11	6	9	0	0	4
subtype3	67	5	37	0	27	18	8	1	2	11	10	4	6	1	1	1

Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

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

Table S71. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	695	2.2 (4.3)
subtype1	327	2.0 (3.3)
subtype2	198	2.5 (5.3)
subtype3	170	2.0 (4.6)

Figure S63. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.012 (Chi-square test), Q value = 0.84

Table S72. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	69	62	8	8	290	185	2	118	23	37	15	1	17
subtype1	28	30	4	2	144	91	1	68	20	14	7	0	9
subtype2	27	18	3	5	61	52	0	28	1	13	5	1	5
subtype3	14	14	1	1	85	42	1	22	2	10	3	0	3

Figure S64. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S73. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	173	431	243

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0226 (logrank test), Q value = 1

Table S74. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	788	95	0.0 - 223.4 (18.6)
subtype1	166	20	0.4 - 159.1 (25.7)
subtype2	400	39	0.0 - 223.4 (17.9)
subtype3	222	36	0.0 - 211.5 (18.0)

Figure S65. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0554 (ANOVA), Q value = 1

Table S75. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	846	58.4 (13.3)
subtype1	172	56.9 (12.9)
subtype2	431	59.5 (13.4)
subtype3	243	57.6 (13.1)

Figure S66. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.327 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	838	9
subtype1	173	0
subtype2	425	6
subtype3	240	3

Figure S67. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.208 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	205	642
subtype1	34	139
subtype2	114	317
subtype3	57	186

Figure S68. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.696 (Chi-square test), Q value = 1

Table S78. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	755	14	76
subtype1	1	154	1	17
subtype2	1	386	7	37
subtype3	0	215	6	22

Figure S69. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.00359 (Chi-square test), Q value = 0.26

Table S79. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	N3C	NX
ALL	248	22	128	1	101	125	32	2	23	50	50	18	29	2	1	15
subtype1	48	10	27	1	18	24	7	1	9	2	11	6	8	1	0	0
subtype2	119	9	62	0	49	80	17	1	10	30	23	8	12	0	0	11
subtype3	81	3	39	0	34	21	8	0	4	18	16	4	9	1	1	4

Figure S70. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.136 (ANOVA), Q value = 1

Table S80. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	708	2.2 (4.4)
subtype1	162	2.8 (6.0)
subtype2	348	1.9 (3.4)
subtype3	198	2.2 (4.4)

Figure S71. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 5.55e-05 (Chi-square test), Q value = 0.0044

Table S81. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	72	63	8	8	290	187	2	120	24	40	14	1	17
subtype1	26	14	1	6	54	35	1	17	4	14	1	0	0
subtype2	36	36	5	1	137	103	1	64	16	13	7	0	12
subtype3	10	13	2	1	99	49	0	39	4	13	6	1	5

Figure S72. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S82. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	194	594	59

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.329 (logrank test), Q value = 1

Table S83. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	788	95	0.0 - 223.4 (18.6)
subtype1	181	28	0.1 - 211.5 (20.2)
subtype2	556	61	0.1 - 223.4 (18.2)
subtype3	51	6	0.0 - 113.8 (17.0)

Figure S73. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0631 (ANOVA), Q value = 1

Table S84. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	846	58.4 (13.3)
subtype1	194	56.7 (12.6)
subtype2	593	59.1 (13.5)
subtype3	59	57.2 (12.3)

Figure S74. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.187 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	838	9
subtype1	193	1
subtype2	588	6
subtype3	57	2

Figure S75. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.385 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	205	642
subtype1	50	144
subtype2	145	449
subtype3	10	49

Figure S76. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.561 (Chi-square test), Q value = 1

Table S87. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	755	14	76
subtype1	0	178	4	12
subtype2	2	526	8	58
subtype3	0	51	2	6

Figure S77. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.464 (Chi-square test), Q value = 1

Table S88. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N0 (I+)	N0 (I-)	N0 (MOL+)	N1	N1A	N1B	N1C	N1MI	N2	N2A	N3	N3A	N3B	N3C	NX
ALL	248	22	128	1	101	125	32	2	23	50	50	18	29	2	1	15
subtype1	66	5	36	0	24	17	7	1	1	11	11	2	8	1	1	3
subtype2	160	17	84	1	70	101	23	1	21	33	36	15	20	1	0	11
subtype3	22	0	8	0	7	7	2	0	1	6	3	1	1	0	0	1

Figure S78. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.813 (ANOVA), Q value = 1

Table S89. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	708	2.2 (4.4)
subtype1	167	2.0 (4.6)
subtype2	496	2.3 (4.3)
subtype3	45	2.2 (4.8)

Figure S79. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.647 (Chi-square test), Q value = 1

Table S90. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	72	63	8	8	290	187	2	120	24	40	14	1	17
subtype1	13	15	2	1	80	37	0	23	3	11	4	0	4
subtype2	57	43	6	7	186	136	2	87	21	28	8	1	12
subtype3	2	5	0	0	24	14	0	10	0	1	2	0	1

Figure S80. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = BRCA-TP.mergedcluster.txt
Clinical data file = BRCA-TP.clin.merged.picked.txt
Number of patients = 869
Number of clustering approaches = 10
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

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] 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] 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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