Breast Invasive Carcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 7 different clustering approaches and 5 clinical features across 851 patients, 13 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death', 'AGE', and 'GENDER'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', and 'GENDER'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	7.48e-05	5.88e-06	0.0354	0.848	0.398
mRNA cHierClus subtypes	0.445	0.00183	0.323	0.224	0.38
METHLYATION CNMF	0.000294	0.00115	0.0202	0.13	0.988
RNAseq CNMF subtypes	0.137	0.0386	0.148	0.388	0.0981
RNAseq cHierClus subtypes	0.187	0.118	0.158	0.142	0.00648
MIRseq CNMF subtypes	0.00773	0.00868	0.222	0.685	0.281
MIRseq cHierClus subtypes	0.0258	0.0131	0.13	0.412	0.282

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	20	34	117	103	120	73	20	42

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 7.48e-05 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	65	0.1 - 223.4 (24.1)
subtype1	18	3	0.3 - 92.0 (14.2)
subtype2	33	3	0.1 - 157.4 (43.4)
subtype3	109	17	0.1 - 177.4 (25.1)
subtype4	98	12	0.1 - 211.5 (21.9)
subtype5	115	10	0.3 - 223.4 (19.0)
subtype6	64	14	0.1 - 189.0 (24.6)
subtype7	19	2	0.2 - 97.5 (36.3)
subtype8	41	4	0.3 - 112.4 (20.0)

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 = 5.88e-06 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	529	57.9 (13.2)
subtype1	20	59.6 (14.1)
subtype2	34	49.9 (10.1)
subtype3	117	58.0 (14.3)
subtype4	103	53.8 (12.6)
subtype5	120	62.0 (12.7)
subtype6	73	58.2 (12.7)
subtype7	20	60.4 (9.9)
subtype8	42	59.9 (12.8)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0354 (Chi-square test)

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

nPatients	FEMALE	MALE
ALL	523	6
subtype1	19	1
subtype2	34	0
subtype3	113	4
subtype4	103	0
subtype5	120	0
subtype6	73	0
subtype7	19	1
subtype8	42	0

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 = 0.848 (Chi-square test)

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

nPatients	NO	YES
ALL	382	147
subtype1	17	3
subtype2	25	9
subtype3	86	31
subtype4	69	34
subtype5	86	34
subtype6	54	19
subtype7	15	5
subtype8	30	12

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.398 (Chi-square test)

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

nPatients	NO	YES
ALL	307	222
subtype1	15	5
subtype2	17	17
subtype3	66	51
subtype4	55	48
subtype5	71	49
subtype6	47	26
subtype7	14	6
subtype8	22	20

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	129	136	264

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.445 (logrank test)

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	65	0.1 - 223.4 (24.1)
subtype1	118	15	0.1 - 211.5 (21.6)
subtype2	134	15	0.3 - 157.4 (27.6)
subtype3	245	35	0.1 - 223.4 (21.1)

Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00183 (ANOVA)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	529	57.9 (13.2)
subtype1	129	55.1 (12.6)
subtype2	136	56.8 (13.0)
subtype3	264	59.8 (13.4)

Figure S7. 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)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	523	6
subtype1	129	0
subtype2	135	1
subtype3	259	5

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

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

P value = 0.224 (Fisher's exact test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	382	147
subtype1	86	43
subtype2	98	38
subtype3	198	66

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.38 (Fisher's exact test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	307	222
subtype1	70	59
subtype2	76	60
subtype3	161	103

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	82	137	54	97	29	52	87

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000294 (logrank test)

Table S14. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	509	61	0.0 - 223.4 (17.9)
subtype1	77	12	0.2 - 211.5 (19.2)
subtype2	129	12	0.3 - 223.4 (12.8)
subtype3	50	10	0.0 - 162.0 (14.0)
subtype4	93	7	0.1 - 177.4 (23.1)
subtype5	29	3	0.1 - 157.4 (20.8)
subtype6	50	9	0.0 - 109.9 (17.9)
subtype7	81	8	0.0 - 194.3 (27.9)

Figure S11. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00115 (ANOVA)

Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	536	57.5 (13.1)
subtype1	82	54.8 (11.9)
subtype2	137	58.7 (12.1)
subtype3	54	59.0 (12.3)
subtype4	97	55.3 (15.0)
subtype5	29	57.1 (15.8)
subtype6	52	63.9 (11.5)
subtype7	85	56.0 (12.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0202 (Chi-square test)

Table S16. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	532	6
subtype1	82	0
subtype2	136	1
subtype3	54	0
subtype4	96	1
subtype5	27	2
subtype6	50	2
subtype7	87	0

Figure S13. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.13 (Chi-square test)

Table S17. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	409	129
subtype1	54	28
subtype2	107	30
subtype3	37	17
subtype4	75	22
subtype5	25	4
subtype6	43	9
subtype7	68	19

Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.988 (Chi-square test)

Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	336	202
subtype1	49	33
subtype2	84	53
subtype3	33	21
subtype4	62	35
subtype5	19	10
subtype6	32	20
subtype7	57	30

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	229	130	392

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.137 (logrank test)

Table S20. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	697	86	0.0 - 223.4 (18.1)
subtype1	208	31	0.0 - 211.5 (18.4)
subtype2	124	10	0.3 - 157.4 (26.3)
subtype3	365	45	0.0 - 223.4 (17.0)

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0386 (ANOVA)

Table S21. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	749	57.7 (13.3)
subtype1	229	56.6 (12.9)
subtype2	128	56.1 (12.5)
subtype3	392	58.9 (13.6)

Figure S17. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.148 (Fisher's exact test)

Table S22. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	744	7
subtype1	229	0
subtype2	129	1
subtype3	386	6

Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.388 (Fisher's exact test)

Table S23. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	568	183
subtype1	170	59
subtype2	94	36
subtype3	304	88

Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0981 (Fisher's exact test)

Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	458	293
subtype1	139	90
subtype2	69	61
subtype3	250	142

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	195	380	176

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.187 (logrank test)

Table S26. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	697	86	0.0 - 223.4 (18.1)
subtype1	179	26	0.0 - 211.5 (16.5)
subtype2	348	44	0.0 - 223.4 (16.8)
subtype3	170	16	0.1 - 157.4 (25.5)

Figure S21. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.118 (ANOVA)

Table S27. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	749	57.7 (13.3)
subtype1	195	56.3 (13.0)
subtype2	380	58.6 (13.6)
subtype3	174	57.3 (12.8)

Figure S22. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.158 (Fisher's exact test)

Table S28. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	744	7
subtype1	195	0
subtype2	374	6
subtype3	175	1

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

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

P value = 0.142 (Fisher's exact test)

Table S29. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	568	183
subtype1	142	53
subtype2	299	81
subtype3	127	49

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00648 (Fisher's exact test)

Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	458	293
subtype1	117	78
subtype2	250	130
subtype3	91	85

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	194	403	210

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00773 (logrank test)

Table S32. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	748	92	0.0 - 223.4 (19.0)
subtype1	186	21	0.3 - 159.1 (24.5)
subtype2	372	39	0.0 - 223.4 (18.0)
subtype3	190	32	0.0 - 211.5 (17.6)

Figure S26. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.00868 (ANOVA)

Table S33. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	805	58.2 (13.3)
subtype1	192	56.0 (12.6)
subtype2	403	59.5 (13.6)
subtype3	210	57.6 (12.9)

Figure S27. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test)

Table S34. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	798	9
subtype1	194	0
subtype2	396	7
subtype3	208	2

Figure S28. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.685 (Fisher's exact test)

Table S35. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	623	184
subtype1	154	40
subtype2	307	96
subtype3	162	48

Figure S29. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.281 (Fisher's exact test)

Table S36. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	508	299
subtype1	116	78
subtype2	251	152
subtype3	141	69

Figure S30. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	223	423	161

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0258 (logrank test)

Table S38. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	748	92	0.0 - 223.4 (19.0)
subtype1	194	26	0.0 - 194.3 (17.0)
subtype2	401	40	0.1 - 223.4 (19.1)
subtype3	153	26	0.1 - 211.5 (20.0)

Figure S31. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0131 (ANOVA)

Table S39. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	805	58.2 (13.3)
subtype1	223	59.4 (13.4)
subtype2	421	58.5 (13.2)
subtype3	161	55.5 (12.8)

Figure S32. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.13 (Fisher's exact test)

Table S40. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	798	9
subtype1	218	5
subtype2	419	4
subtype3	161	0

Figure S33. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.412 (Fisher's exact test)

Table S41. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	623	184
subtype1	179	44
subtype2	323	100
subtype3	121	40

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.282 (Fisher's exact test)

Table S42. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	508	299
subtype1	150	73
subtype2	258	165
subtype3	100	61

Figure S35. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = BRCA.mergedcluster.txt
Clinical data file = BRCA.clin.merged.picked.txt
Number of patients = 851
Number of clustering approaches = 7
Number of selected clinical features = 5
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

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)

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