Correlation between aggregated molecular cancer subtypes and selected clinical features

Adrenocortical Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1W094M8

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 7 clinical features across 82 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'PATHOLOGY.N.STAGE'.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'GENDER'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death' and 'NEOPLASM.DISEASESTAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 12 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.0019 (0.0893)	0.298 (1.00)	0.102 (1.00)	0.698 (1.00)	0.00154 (0.0739)	0.686 (1.00)	0.683 (1.00)
METHLYATION CNMF	0.00134 (0.0672)	0.337 (1.00)	0.184 (1.00)	0.112 (1.00)	0.115 (1.00)	0.00116 (0.0592)	0.797 (1.00)
RNAseq CNMF subtypes	0.00144 (0.0706)	0.938 (1.00)	0.0262 (1.00)	0.157 (1.00)	0.0719 (1.00)	0.322 (1.00)	0.937 (1.00)
RNAseq cHierClus subtypes	0.00103 (0.0548)	0.656 (1.00)	0.00503 (0.226)	0.0357 (1.00)	0.0609 (1.00)	0.473 (1.00)	0.281 (1.00)
MIRSEQ CNMF	0.0147 (0.617)	0.186 (1.00)	0.00054 (0.0302)	0.00595 (0.262)	0.0399 (1.00)	0.123 (1.00)	0.3 (1.00)
MIRSEQ CHIERARCHICAL	0.0839 (1.00)	0.275 (1.00)	0.00074 (0.04)	0.0913 (1.00)	0.361 (1.00)	0.323 (1.00)	0.661 (1.00)
MIRseq Mature CNMF subtypes	0.00879 (0.378)	0.256 (1.00)	0.00109 (0.0567)	0.0329 (1.00)	0.0616 (1.00)	0.125 (1.00)	0.499 (1.00)
MIRseq Mature cHierClus subtypes	0.00219 (0.101)	0.373 (1.00)	0.00064 (0.0352)	0.0349 (1.00)	0.355 (1.00)	0.952 (1.00)	0.452 (1.00)

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

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

Cluster Labels	1	2	3	4	5
Number of samples	27	21	14	9	9

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

P value = 0.0019 (logrank test), Q value = 0.089

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	79	27	4.1 - 153.6 (31.2)
subtype1	26	14	4.9 - 128.1 (33.3)
subtype2	21	1	6.9 - 153.6 (31.2)
subtype3	14	7	10.2 - 64.1 (20.8)
subtype4	9	4	4.1 - 63.2 (27.3)
subtype5	9	1	12.0 - 91.5 (46.8)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.298 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	80	46.9 (16.3)
subtype1	27	47.1 (18.2)
subtype2	21	51.0 (15.1)
subtype3	14	41.1 (16.6)
subtype4	9	51.9 (11.7)
subtype5	9	40.2 (14.8)

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

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

P value = 0.102 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	34	17	17
subtype1	4	8	7	7
subtype2	2	12	6	1
subtype3	0	4	3	7
subtype4	1	4	0	1
subtype5	1	6	1	1

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

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

P value = 0.698 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	39	11	18
subtype1	4	9	5	8
subtype2	2	13	3	3
subtype3	0	7	2	5
subtype4	1	4	0	1
subtype5	1	6	1	1

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00154 (Fisher's exact test), Q value = 0.074

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

nPatients	0	1
ALL	67	10
subtype1	24	2
subtype2	20	1
subtype3	7	7
subtype4	7	0
subtype5	9	0

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.686 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	52	28
subtype1	19	8
subtype2	13	8
subtype3	10	4
subtype4	4	5
subtype5	6	3

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	27
subtype1	3	11
subtype2	1	7
subtype3	4	6
subtype4	0	1
subtype5	0	2

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S9. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	18	11	21	17	11

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00134 (logrank test), Q value = 0.067

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	26	4.1 - 153.6 (32.0)
subtype1	18	12	4.1 - 120.3 (20.1)
subtype2	11	3	12.0 - 128.1 (50.1)
subtype3	21	7	6.8 - 153.6 (31.2)
subtype4	17	3	6.9 - 121.2 (23.8)
subtype5	11	1	12.6 - 91.5 (22.2)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.337 (Kruskal-Wallis (anova)), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.4 (16.1)
subtype1	18	40.3 (20.0)
subtype2	11	53.2 (14.8)
subtype3	21	47.2 (14.5)
subtype4	17	49.3 (12.7)
subtype5	11	43.9 (16.4)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.184 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	16
subtype1	1	7	6	4
subtype2	1	3	4	3
subtype3	1	7	3	6
subtype4	3	7	3	3
subtype5	2	9	0	0

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.112 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	9	18
subtype1	1	9	3	5
subtype2	1	5	4	1
subtype3	1	8	1	7
subtype4	3	7	1	5
subtype5	2	9	0	0

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.115 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	10
subtype1	14	4
subtype2	9	2
subtype3	14	4
subtype4	16	0
subtype5	11	0

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00116 (Fisher's exact test), Q value = 0.059

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	13	5
subtype2	9	2
subtype3	7	14
subtype4	9	8
subtype5	11	0

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.797 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	28
subtype1	3	6
subtype2	2	6
subtype3	1	7
subtype4	2	5
subtype5	0	4

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S17. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	24	14	17	22

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00144 (logrank test), Q value = 0.071

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	77	25	4.1 - 153.6 (32.7)
subtype1	24	14	4.1 - 128.1 (20.4)
subtype2	14	5	6.8 - 153.6 (33.8)
subtype3	17	5	12.0 - 106.5 (50.1)
subtype4	22	1	6.9 - 121.2 (28.4)

Figure S15. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.938 (Kruskal-Wallis (anova)), Q value = 1

Table S19. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	77	46.7 (15.9)
subtype1	24	46.7 (18.2)
subtype2	14	47.3 (14.9)
subtype3	17	45.0 (13.1)
subtype4	22	47.8 (16.8)

Figure S16. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0262 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	15
subtype1	3	8	8	5
subtype2	0	6	2	6
subtype3	1	5	5	3
subtype4	4	14	1	1

Figure S17. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.157 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	8	18
subtype1	3	10	4	7
subtype2	0	8	2	4
subtype3	1	6	1	6
subtype4	4	14	1	1

Figure S18. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0719 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	9
subtype1	21	3
subtype2	10	4
subtype3	12	2
subtype4	21	0

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.322 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	29
subtype1	16	8
subtype2	11	3
subtype3	8	9
subtype4	13	9

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.937 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	28
subtype1	2	11
subtype2	2	7
subtype3	2	5
subtype4	1	5

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S25. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	23	23	7	24

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00103 (logrank test), Q value = 0.055

Table S26. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	77	25	4.1 - 153.6 (32.7)
subtype1	23	14	4.1 - 128.1 (33.8)
subtype2	23	7	6.8 - 153.6 (39.6)
subtype3	7	3	10.1 - 87.9 (19.0)
subtype4	24	1	6.9 - 121.2 (27.5)

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.656 (Kruskal-Wallis (anova)), Q value = 1

Table S27. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	77	46.7 (15.9)
subtype1	23	47.3 (19.2)
subtype2	23	45.4 (15.0)
subtype3	7	41.3 (14.9)
subtype4	24	49.1 (14.1)

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00503 (Fisher's exact test), Q value = 0.23

Table S28. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	15
subtype1	2	9	7	5
subtype2	0	8	5	8
subtype3	2	1	1	2
subtype4	4	15	3	0

Figure S24. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0357 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	8	18
subtype1	2	12	4	5
subtype2	0	10	2	9
subtype3	2	1	1	2
subtype4	4	15	1	2

Figure S25. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0609 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	9
subtype1	19	4
subtype2	16	5
subtype3	6	0
subtype4	23	0

Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.473 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	29
subtype1	17	6
subtype2	13	10
subtype3	5	2
subtype4	13	11

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.281 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	28
subtype1	2	10
subtype2	3	8
subtype3	2	3
subtype4	0	7

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S33. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	38	15	25

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0147 (logrank test), Q value = 0.62

Table S34. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	26	4.1 - 153.6 (32.0)
subtype1	38	15	4.1 - 120.3 (29.7)
subtype2	15	8	8.3 - 153.6 (33.8)
subtype3	25	3	6.9 - 121.2 (46.8)

Figure S29. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.186 (Kruskal-Wallis (anova)), Q value = 1

Table S35. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.4 (16.1)
subtype1	38	43.0 (15.8)
subtype2	15	50.9 (17.4)
subtype3	25	49.0 (15.1)

Figure S30. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00054 (Fisher's exact test), Q value = 0.03

Table S36. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	16
subtype1	3	12	12	9
subtype2	1	4	2	7
subtype3	4	17	2	0

Figure S31. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00595 (Fisher's exact test), Q value = 0.26

Table S37. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	9	18
subtype1	3	16	4	13
subtype2	1	5	3	5
subtype3	4	17	2	0

Figure S32. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0399 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	10
subtype1	29	7
subtype2	11	3
subtype3	24	0

Figure S33. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.123 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	25	13
subtype2	12	3
subtype3	12	13

Figure S34. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.3 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	28
subtype1	5	18
subtype2	3	5
subtype3	0	5

Figure S35. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S41. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	17	25	12	20	4

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0839 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	26	4.1 - 153.6 (32.0)
subtype1	17	7	4.1 - 93.4 (41.3)
subtype2	25	7	6.9 - 153.6 (33.8)
subtype3	12	0	10.1 - 106.5 (28.4)
subtype4	20	10	4.9 - 128.1 (25.4)
subtype5	4	2	23.3 - 53.0 (38.4)

Figure S36. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.275 (Kruskal-Wallis (anova)), Q value = 1

Table S43. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.4 (16.1)
subtype1	17	46.3 (14.3)
subtype2	25	47.9 (15.2)
subtype3	12	40.5 (15.9)
subtype4	20	45.4 (18.8)
subtype5	4	60.8 (9.8)

Figure S37. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00074 (Fisher's exact test), Q value = 0.04

Table S44. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	16
subtype1	0	5	7	4
subtype2	1	17	0	6
subtype3	4	3	2	1
subtype4	3	6	7	3
subtype5	0	2	0	2

Figure S38. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0913 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	9	18
subtype1	0	6	4	6
subtype2	1	17	1	5
subtype3	4	3	1	2
subtype4	3	9	3	4
subtype5	0	3	0	1

Figure S39. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.361 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	10
subtype1	13	3
subtype2	23	2
subtype3	10	0
subtype4	15	4
subtype5	3	1

Figure S40. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S47. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	7	10
subtype2	16	9
subtype3	9	3
subtype4	14	6
subtype5	3	1

Figure S41. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.661 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	28
subtype1	3	4
subtype2	2	6
subtype3	1	6
subtype4	2	9
subtype5	0	3

Figure S42. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S49. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	39	17	22

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

P value = 0.00879 (logrank test), Q value = 0.38

Table S50. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	26	4.1 - 153.6 (32.0)
subtype1	39	15	4.1 - 120.3 (29.0)
subtype2	17	10	12.6 - 153.6 (38.9)
subtype3	22	1	6.9 - 121.2 (30.2)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.256 (Kruskal-Wallis (anova)), Q value = 1

Table S51. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.4 (16.1)
subtype1	39	43.4 (15.8)
subtype2	17	50.7 (17.6)
subtype3	22	48.5 (14.8)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00109 (Fisher's exact test), Q value = 0.057

Table S52. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	16
subtype1	3	12	13	9
subtype2	1	7	1	7
subtype3	4	14	2	0

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0329 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	9	18
subtype1	3	16	5	13
subtype2	1	8	2	5
subtype3	4	14	2	0

Figure S46. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0616 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	10
subtype1	30	7
subtype2	13	3
subtype3	21	0

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.125 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	26	13
subtype2	13	4
subtype3	10	12

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.499 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	28
subtype1	5	18
subtype2	3	6
subtype3	0	4

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S57. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	9	23	9	18	9	10

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

P value = 0.00219 (logrank test), Q value = 0.1

Table S58. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	26	4.1 - 153.6 (32.0)
subtype1	9	6	4.1 - 64.1 (30.3)
subtype2	23	5	6.9 - 153.6 (38.9)
subtype3	9	0	17.4 - 106.5 (46.5)
subtype4	18	9	4.9 - 120.3 (20.4)
subtype5	9	5	12.6 - 128.1 (37.1)
subtype6	10	1	10.1 - 93.4 (30.2)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.373 (Kruskal-Wallis (anova)), Q value = 1

Table S59. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.4 (16.1)
subtype1	9	44.4 (15.4)
subtype2	23	47.9 (15.8)
subtype3	9	41.8 (12.0)
subtype4	18	44.5 (19.4)
subtype5	9	56.9 (10.0)
subtype6	10	43.2 (17.5)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00064 (Fisher's exact test), Q value = 0.035

Table S60. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	33	16	16
subtype1	0	3	4	2
subtype2	1	17	0	4
subtype3	1	2	3	2
subtype4	1	6	7	3
subtype5	2	2	0	5
subtype6	3	3	2	0

Figure S52. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0349 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	8	38	9	18
subtype1	0	4	2	3
subtype2	1	17	1	3
subtype3	1	2	1	4
subtype4	1	9	3	4
subtype5	2	3	0	4
subtype6	3	3	2	0

Figure S53. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.355 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	64	10
subtype1	7	2
subtype2	21	2
subtype3	8	0
subtype4	13	4
subtype5	7	2
subtype6	8	0

Figure S54. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.952 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	6	3
subtype2	15	8
subtype3	5	4
subtype4	12	6
subtype5	6	3
subtype6	5	5

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.452 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	28
subtype1	3	3
subtype2	2	5
subtype3	1	4
subtype4	2	7
subtype5	0	6
subtype6	0	3

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Methods & Data

Input

Cluster data file = ACC-TP.mergedcluster.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 82
Number of clustering approaches = 8
Number of selected clinical features = 7
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

Fisher's exact test

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

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

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

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

[4] 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)

[5] 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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