Breast Invasive Carcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Breast Invasive Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 4 clinical features across 866 patients, 7 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 'CN CNMF'. 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 do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 4 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.23 (1.00)	0.000889 (0.0329)	0.33 (1.00)	0.635 (1.00)
mRNA cHierClus subtypes	0.65 (1.00)	0.000537 (0.0204)	0.323 (1.00)	0.16 (1.00)
CN CNMF	0.254 (1.00)	0.0503 (1.00)	0.000119 (0.00476)	0.233 (1.00)
METHLYATION CNMF	0.235 (1.00)	0.00191 (0.0667)	0.0441 (1.00)	0.204 (1.00)
RPPA CNMF subtypes	0.0438 (1.00)	0.0191 (0.591)	0.058 (1.00)	0.944 (1.00)
RPPA cHierClus subtypes	0.238 (1.00)	0.000121 (0.00476)	0.187 (1.00)	0.726 (1.00)
RNAseq CNMF subtypes	0.258 (1.00)	0.012 (0.385)	0.0682 (1.00)	0.458 (1.00)
RNAseq cHierClus subtypes	0.234 (1.00)	0.00176 (0.0633)	0.0578 (1.00)	0.169 (1.00)
MIRseq CNMF subtypes	0.00629 (0.214)	0.0286 (0.858)	0.683 (1.00)	0.424 (1.00)
MIRseq cHierClus subtypes	0.186 (1.00)	0.00927 (0.306)	0.359 (1.00)	0.687 (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 = 0.033

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 = 0.635 (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	147	380
subtype1	70	198
subtype2	27	66
subtype3	50	116

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S6. 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 S7. 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 S5. 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 = 0.02

Table S8. 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 S6. 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 S9. 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 S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.16 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	147	380
subtype1	43	84
subtype2	65	199
subtype3	39	97

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'CN CNMF'

Table S11. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	316	72	196	204	55

'CN CNMF' versus 'Time to Death'

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

Table S12. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	785	94	0.0 - 223.4 (18.4)
subtype1	295	31	0.0 - 223.4 (20.2)
subtype2	71	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 S9. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.0503 (ANOVA), Q value = 1

Table S13. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	842	58.5 (13.2)
subtype1	315	58.1 (12.9)
subtype2	72	61.1 (11.3)
subtype3	196	59.0 (14.6)
subtype4	204	57.0 (13.0)
subtype5	55	61.7 (12.3)

Figure S10. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.000119 (Chi-square test), Q value = 0.0048

Table S14. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	834	9
subtype1	316	0
subtype2	71	1
subtype3	188	8
subtype4	204	0
subtype5	55	0

Figure S11. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.233 (Chi-square test), Q value = 1

Table S15. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	197	646
subtype1	63	253
subtype2	19	53
subtype3	43	153
subtype4	56	148
subtype5	16	39

Figure S12. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S16. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6
Number of samples	96	138	78	125	33	82

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.235 (logrank test), Q value = 1

Table S17. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	61	0.0 - 223.4 (17.9)
subtype1	91	14	0.2 - 211.5 (19.2)
subtype2	130	13	0.3 - 223.4 (13.7)
subtype3	76	12	0.0 - 109.9 (17.7)
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 S13. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00191 (ANOVA), Q value = 0.067

Table S18. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	57.8 (13.1)
subtype1	96	56.3 (13.1)
subtype2	138	59.6 (12.6)
subtype3	78	62.3 (11.7)
subtype4	125	55.4 (14.4)
subtype5	33	55.7 (13.7)
subtype6	81	56.5 (11.7)

Figure S14. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0441 (Chi-square test), Q value = 1

Table S19. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	546	6
subtype1	96	0
subtype2	137	1
subtype3	76	2
subtype4	124	1
subtype5	31	2
subtype6	82	0

Figure S15. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.204 (Chi-square test), Q value = 1

Table S20. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	136	416
subtype1	29	67
subtype2	32	106
subtype3	16	62
subtype4	37	88
subtype5	4	29
subtype6	18	64

Figure S16. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	151	134	123

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0438 (logrank test), Q value = 1

Table S22. 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 (24.3)
subtype2	130	11	0.2 - 146.5 (17.4)
subtype3	115	11	0.3 - 189.0 (28.5)

Figure S17. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0191 (ANOVA), Q value = 0.59

Table S23. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	151	56.3 (13.1)
subtype2	134	60.4 (13.8)
subtype3	123	57.0 (11.9)

Figure S18. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.058 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	403	5
subtype1	151	0
subtype2	130	4
subtype3	122	1

Figure S19. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.944 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	124	284
subtype1	46	105
subtype2	42	92
subtype3	36	87

Figure S20. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	135	162	111

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.238 (logrank test), Q value = 1

Table S27. 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	120	18	0.1 - 189.0 (23.5)
subtype2	151	17	0.2 - 129.7 (19.9)
subtype3	109	9	0.2 - 173.0 (25.3)

Figure S21. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000121 (ANOVA), Q value = 0.0048

Table S28. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	135	54.3 (13.0)
subtype2	162	60.7 (13.4)
subtype3	111	58.0 (11.7)

Figure S22. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S29. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	403	5
subtype1	135	0
subtype2	158	4
subtype3	110	1

Figure S23. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.726 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	124	284
subtype1	40	95
subtype2	47	115
subtype3	37	74

Figure S24. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	239	155	440

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.258 (logrank test), Q value = 1

Table S32. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	777	94	0.0 - 223.4 (18.9)
subtype1	221	33	0.0 - 211.5 (19.3)
subtype2	149	12	0.3 - 194.3 (25.4)
subtype3	407	49	0.0 - 223.4 (17.0)

Figure S25. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.012 (ANOVA), Q value = 0.38

Table S33. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	833	58.2 (13.2)
subtype1	239	57.2 (12.8)
subtype2	154	56.2 (12.4)
subtype3	440	59.4 (13.5)

Figure S26. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0682 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	825	9
subtype1	239	0
subtype2	154	1
subtype3	432	8

Figure S27. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S35. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	197	637
subtype1	57	182
subtype2	42	113
subtype3	98	342

Figure S28. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S36. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	417	218	199

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.234 (logrank test), Q value = 1

Table S37. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	777	94	0.0 - 223.4 (18.9)
subtype1	384	47	0.0 - 223.4 (16.3)
subtype2	210	19	0.1 - 194.3 (25.5)
subtype3	183	28	0.0 - 211.5 (20.0)

Figure S29. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00176 (ANOVA), Q value = 0.063

Table S38. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	833	58.2 (13.2)
subtype1	417	59.8 (13.3)
subtype2	217	56.4 (13.0)
subtype3	199	56.7 (12.8)

Figure S30. 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 S39. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	825	9
subtype1	409	8
subtype2	217	1
subtype3	199	0

Figure S31. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.169 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	197	637
subtype1	87	330
subtype2	58	160
subtype3	52	147

Figure S32. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S41. Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	185	411	242

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00629 (logrank test), Q value = 0.21

Table S42. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	779	95	0.0 - 223.4 (18.9)
subtype1	178	19	0.4 - 159.1 (21.0)
subtype2	381	37	0.0 - 223.4 (17.9)
subtype3	220	39	0.0 - 211.5 (19.2)

Figure S33. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0286 (ANOVA), Q value = 0.86

Table S43. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	837	58.4 (13.3)
subtype1	184	57.3 (12.7)
subtype2	411	59.7 (13.7)
subtype3	242	57.1 (12.8)

Figure S34. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.683 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	829	9
subtype1	184	1
subtype2	405	6
subtype3	240	2

Figure S35. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.424 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	193	645
subtype1	37	148
subtype2	102	309
subtype3	54	188

Figure S36. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	205	268	365

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.186 (logrank test), Q value = 1

Table S47. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	779	95	0.0 - 223.4 (18.9)
subtype1	192	30	0.0 - 211.5 (19.7)
subtype2	237	31	0.1 - 223.4 (17.8)
subtype3	350	34	0.0 - 177.4 (18.6)

Figure S37. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA), Q value = 0.31

Table S48. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	837	58.4 (13.3)
subtype1	205	56.9 (12.7)
subtype2	268	60.4 (14.3)
subtype3	364	57.8 (12.7)

Figure S38. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.359 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	829	9
subtype1	204	1
subtype2	263	5
subtype3	362	3

Figure S39. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.687 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	193	645
subtype1	49	156
subtype2	65	203
subtype3	79	286

Figure S40. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = BRCA-TP.mergedcluster.txt
Clinical data file = BRCA-TP.clin.merged.picked.txt
Number of patients = 866
Number of clustering approaches = 10
Number of selected clinical features = 4
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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