Correlation between aggregated molecular cancer subtypes and selected clinical features

Adrenocortical Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C17W6B12

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 8 clinical features across 92 patients, 10 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'GENDER'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 8 subtypes that do not correlate to any clinical features.
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'.
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 'Time to Death', 'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.T.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 10 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER	RACE	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	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.00671 (0.443)	0.0265 (1.00)	0.226 (1.00)	0.745 (1.00)	0.00568 (0.392)	0.73 (1.00)	0.817 (1.00)	0.244 (1.00)
METHLYATION CNMF	0.000345 (0.0267)	0.323 (1.00)	0.225 (1.00)	0.167 (1.00)	0.0969 (1.00)	0.00039 (0.0296)	0.551 (1.00)	0.747 (1.00)
RPPA CNMF subtypes	0.0187 (1.00)	0.442 (1.00)	0.133 (1.00)	0.316 (1.00)	0.216 (1.00)	0.201 (1.00)	0.418 (1.00)	0.796 (1.00)
RPPA cHierClus subtypes	0.0529 (1.00)	0.491 (1.00)	0.098 (1.00)	0.17 (1.00)	0.552 (1.00)	0.468 (1.00)	0.263 (1.00)	0.757 (1.00)
RNAseq CNMF subtypes	0.000338 (0.0267)	0.906 (1.00)	0.011 (0.706)	0.0897 (1.00)	0.047 (1.00)	0.238 (1.00)	0.303 (1.00)	0.944 (1.00)
RNAseq cHierClus subtypes	0.00033 (0.0264)	0.623 (1.00)	0.0057 (0.392)	0.0445 (1.00)	0.0437 (1.00)	0.364 (1.00)	0.00938 (0.61)	0.241 (1.00)
MIRSEQ CNMF	0.00571 (0.392)	0.219 (1.00)	0.00039 (0.0296)	0.00513 (0.359)	0.0374 (1.00)	0.14 (1.00)	1 (1.00)	0.381 (1.00)
MIRSEQ CHIERARCHICAL	0.0294 (1.00)	0.265 (1.00)	0.00082 (0.0599)	0.124 (1.00)	0.292 (1.00)	0.326 (1.00)	0.105 (1.00)	0.7 (1.00)
MIRseq Mature CNMF subtypes	0.00034 (0.0267)	0.412 (1.00)	0.00059 (0.0437)	0.0009 (0.0648)	0.027 (1.00)	0.0634 (1.00)	1 (1.00)	0.865 (1.00)
MIRseq Mature cHierClus subtypes	0.0715 (1.00)	0.969 (1.00)	0.00348 (0.247)	0.09 (1.00)	0.156 (1.00)	0.364 (1.00)	0.408 (1.00)	1 (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
Number of samples	36	26	16	12

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

P value = 0.00671 (logrank test), Q value = 0.44

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	89	31	4.1 - 153.6 (36.3)
subtype1	35	17	4.9 - 152.2 (33.8)
subtype2	26	3	5.2 - 153.6 (31.5)
subtype3	16	9	12.0 - 69.1 (33.2)
subtype4	12	2	4.1 - 91.5 (54.9)

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 = 0.0265 (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	90	47.1 (16.5)
subtype1	36	48.5 (17.9)
subtype2	26	53.1 (14.3)
subtype3	16	39.2 (15.1)
subtype4	12	40.6 (12.8)

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 = 0.226 (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	9	43	18	18
subtype1	5	15	8	7
subtype2	2	15	6	3
subtype3	0	5	3	7
subtype4	2	8	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 = 0.745 (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	9	48	11	20
subtype1	5	16	5	9
subtype2	2	16	3	5
subtype3	0	8	2	5
subtype4	2	8	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 = 0.00568 (Fisher's exact test), Q value = 0.39

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

nPatients	0	1
ALL	78	10
subtype1	33	2
subtype2	24	2
subtype3	9	6
subtype4	12	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 = 0.73 (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	59	31
subtype1	26	10
subtype2	16	10
subtype3	10	6
subtype4	7	5

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 'RACE'

P value = 0.817 (Fisher's exact test), Q value = 1

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	1	76
subtype1	2	1	30
subtype2	0	0	24
subtype3	0	0	13
subtype4	0	0	9

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.244 (Fisher's exact test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	37
subtype1	3	16
subtype2	1	12
subtype3	4	6
subtype4	0	3

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	18	11	22	18	11

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000345 (logrank test), Q value = 0.027

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	28	4.1 - 153.6 (36.1)
subtype1	18	13	4.1 - 127.5 (20.1)
subtype2	11	3	18.1 - 152.2 (50.1)
subtype3	22	8	6.8 - 153.6 (37.8)
subtype4	18	3	6.9 - 121.2 (29.5)
subtype5	11	1	12.6 - 91.5 (36.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.323 (Kruskal-Wallis (anova)), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	80	46.4 (15.9)
subtype1	18	40.3 (20.0)
subtype2	11	53.2 (14.8)
subtype3	22	46.8 (14.2)
subtype4	18	49.4 (12.4)
subtype5	11	43.9 (16.4)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.225 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	36	16	16
subtype1	1	7	6	4
subtype2	1	3	4	3
subtype3	2	8	3	6
subtype4	3	9	3	3
subtype5	2	9	0	0

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.167 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	41	9	18
subtype1	1	9	3	5
subtype2	1	5	4	1
subtype3	2	9	1	7
subtype4	3	9	1	5
subtype5	2	9	0	0

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0969 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	67	10
subtype1	14	4
subtype2	9	2
subtype3	15	4
subtype4	18	0
subtype5	11	0

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00039 (Fisher's exact test), Q value = 0.03

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	31
subtype1	13	5
subtype2	9	2
subtype3	7	15
subtype4	9	9
subtype5	11	0

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.551 (Fisher's exact test), Q value = 1

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	67
subtype1	0	0	18
subtype2	0	0	9
subtype3	0	0	17
subtype4	1	1	15
subtype5	0	0	8

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.747 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	30
subtype1	3	6
subtype2	2	6
subtype3	1	8
subtype4	2	6
subtype5	0	4

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

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S19. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	14	12	11	9

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0187 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	13	4.1 - 153.6 (42.0)
subtype1	14	7	4.1 - 69.1 (34.9)
subtype2	12	2	4.9 - 72.4 (47.8)
subtype3	11	1	15.4 - 121.2 (78.4)
subtype4	9	3	6.9 - 153.6 (23.3)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.442 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	46	47.2 (14.4)
subtype1	14	45.9 (17.0)
subtype2	12	46.4 (14.2)
subtype3	11	44.5 (10.7)
subtype4	9	53.4 (14.9)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.133 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	25	10	8
subtype1	0	7	4	3
subtype2	0	6	4	2
subtype3	2	8	0	0
subtype4	0	4	2	3

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

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

P value = 0.316 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	2	29	7	7
subtype1	0	8	3	3
subtype2	0	7	3	2
subtype3	2	8	0	0
subtype4	0	6	1	2

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.216 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	39	6
subtype1	12	2
subtype2	11	1
subtype3	10	0
subtype4	6	3

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.201 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	28	18
subtype1	11	3
subtype2	7	5
subtype3	4	7
subtype4	6	3

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.418 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	35
subtype1	0	0	11
subtype2	1	0	9
subtype3	0	1	7
subtype4	0	0	8

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.796 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	17
subtype1	3	4
subtype2	1	6
subtype3	1	3
subtype4	1	4

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S28. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	8	7	7	3	4	4	7	6

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0529 (logrank test), Q value = 1

Table S29. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	13	4.1 - 153.6 (42.0)
subtype1	8	4	4.1 - 69.1 (37.8)
subtype2	7	3	8.3 - 68.8 (30.3)
subtype3	7	0	6.9 - 88.9 (39.5)
subtype4	3	1	4.9 - 44.8 (16.1)
subtype5	4	3	12.6 - 153.6 (18.6)
subtype6	4	0	18.1 - 121.2 (79.8)
subtype7	7	1	12.6 - 106.5 (66.5)
subtype8	6	1	20.2 - 72.4 (50.7)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.491 (Kruskal-Wallis (anova)), Q value = 1

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	46	47.2 (14.4)
subtype1	8	47.2 (15.0)
subtype2	7	41.6 (14.8)
subtype3	7	52.4 (14.2)
subtype4	3	31.7 (7.6)
subtype5	4	54.0 (15.4)
subtype6	4	45.2 (10.8)
subtype7	7	48.3 (15.4)
subtype8	6	50.7 (15.6)

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.098 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	25	10	8
subtype1	0	5	1	2
subtype2	0	1	3	3
subtype3	1	6	0	0
subtype4	0	1	2	0
subtype5	0	1	1	2
subtype6	1	2	1	0
subtype7	0	5	1	0
subtype8	0	4	1	1

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.17 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	2	29	7	7
subtype1	0	6	1	1
subtype2	0	2	2	3
subtype3	1	6	0	0
subtype4	0	2	1	0
subtype5	0	1	1	2
subtype6	1	2	1	0
subtype7	0	5	0	1
subtype8	0	5	1	0

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.552 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	39	6
subtype1	7	1
subtype2	5	2
subtype3	7	0
subtype4	2	1
subtype5	3	1
subtype6	4	0
subtype7	6	0
subtype8	5	1

Figure S29. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.468 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	28	18
subtype1	6	2
subtype2	6	1
subtype3	3	4
subtype4	2	1
subtype5	3	1
subtype6	1	3
subtype7	3	4
subtype8	4	2

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.263 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	35
subtype1	0	0	6
subtype2	0	0	7
subtype3	0	1	3
subtype4	0	0	3
subtype5	0	0	4
subtype6	0	0	2
subtype7	0	0	6
subtype8	1	0	4

Figure S31. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.757 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	17
subtype1	2	3
subtype2	1	4
subtype3	1	1
subtype4	1	1
subtype5	1	2
subtype6	0	0
subtype7	0	2
subtype8	0	4

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S37. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	24	14	18	23

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000338 (logrank test), Q value = 0.027

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	79	27	4.1 - 153.6 (36.3)
subtype1	24	15	4.1 - 152.2 (20.4)
subtype2	14	6	6.8 - 153.6 (36.2)
subtype3	18	5	12.0 - 106.5 (48.3)
subtype4	23	1	6.9 - 121.2 (39.5)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.906 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	79	46.7 (15.8)
subtype1	24	46.7 (18.2)
subtype2	14	47.3 (14.9)
subtype3	18	44.7 (12.8)
subtype4	23	47.9 (16.5)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.011 (Fisher's exact test), Q value = 0.71

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	36	16	15
subtype1	3	8	8	5
subtype2	0	6	2	6
subtype3	1	6	5	3
subtype4	5	16	1	1

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

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

P value = 0.0897 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	41	8	18
subtype1	3	10	4	7
subtype2	0	8	2	4
subtype3	1	7	1	6
subtype4	5	16	1	1

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.047 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	67	9
subtype1	21	3
subtype2	10	4
subtype3	13	2
subtype4	23	0

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.238 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	31
subtype1	16	8
subtype2	11	3
subtype3	8	10
subtype4	13	10

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.303 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	66
subtype1	0	0	24
subtype2	0	0	12
subtype3	0	0	16
subtype4	1	1	14

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

Table S45. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	30
subtype1	2	11
subtype2	2	7
subtype3	2	6
subtype4	1	6

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S46. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	23	24	7	25

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00033 (logrank test), Q value = 0.026

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	79	27	4.1 - 153.6 (36.3)
subtype1	23	15	4.1 - 152.2 (33.8)
subtype2	24	8	6.8 - 153.6 (40.5)
subtype3	7	3	12.6 - 95.2 (20.2)
subtype4	25	1	6.9 - 121.2 (39.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.623 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	79	46.7 (15.8)
subtype1	23	47.3 (19.2)
subtype2	24	45.1 (14.7)
subtype3	7	41.3 (14.9)
subtype4	25	49.2 (13.8)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0057 (Fisher's exact test), Q value = 0.39

Table S49. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	36	16	15
subtype1	2	9	7	5
subtype2	0	9	5	8
subtype3	2	2	1	2
subtype4	5	16	3	0

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

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

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	41	8	18
subtype1	2	12	4	5
subtype2	0	11	2	9
subtype3	2	2	1	2
subtype4	5	16	1	2

Figure S44. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0437 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	67	9
subtype1	19	4
subtype2	17	5
subtype3	7	0
subtype4	24	0

Figure S45. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.364 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	31
subtype1	17	6
subtype2	13	11
subtype3	5	2
subtype4	13	12

Figure S46. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00938 (Fisher's exact test), Q value = 0.61

Table S53. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	66
subtype1	0	0	22
subtype2	0	0	20
subtype3	1	1	5
subtype4	0	0	19

Figure S47. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.241 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	30
subtype1	2	10
subtype2	3	9
subtype3	2	3
subtype4	0	8

Figure S48. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S55. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	38	16	26

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00571 (logrank test), Q value = 0.39

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	28	4.1 - 153.6 (36.1)
subtype1	38	16	4.1 - 127.5 (32.2)
subtype2	16	9	8.3 - 153.6 (29.2)
subtype3	26	3	6.9 - 121.2 (45.1)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.219 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	80	46.4 (15.9)
subtype1	38	43.0 (15.8)
subtype2	16	50.1 (17.0)
subtype3	26	49.0 (14.8)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00039 (Fisher's exact test), Q value = 0.03

Table S58. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	36	16	16
subtype1	3	13	12	9
subtype2	1	5	2	7
subtype3	5	18	2	0

Figure S51. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00513 (Fisher's exact test), Q value = 0.36

Table S59. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	41	9	18
subtype1	3	17	4	13
subtype2	1	6	3	5
subtype3	5	18	2	0

Figure S52. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0374 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	67	10
subtype1	30	7
subtype2	12	3
subtype3	25	0

Figure S53. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.14 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	31
subtype1	25	13
subtype2	12	4
subtype3	12	14

Figure S54. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

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

Table S62. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	67
subtype1	1	1	33
subtype2	0	0	15
subtype3	0	0	19

Figure S55. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.381 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	30
subtype1	5	18
subtype2	3	6
subtype3	0	6

Figure S56. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S64. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	17	27	12	20	4

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0294 (logrank test), Q value = 1

Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	28	4.1 - 153.6 (36.1)
subtype1	17	8	4.1 - 100.2 (44.5)
subtype2	27	7	6.9 - 153.6 (36.3)
subtype3	12	0	18.1 - 106.5 (37.5)
subtype4	20	11	4.9 - 152.2 (30.9)
subtype5	4	2	23.3 - 53.0 (44.3)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.265 (Kruskal-Wallis (anova)), Q value = 1

Table S66. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	80	46.4 (15.9)
subtype1	17	46.3 (14.3)
subtype2	27	47.7 (14.7)
subtype3	12	40.5 (15.9)
subtype4	20	45.4 (18.8)
subtype5	4	60.8 (9.8)

Figure S58. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00082 (Fisher's exact test), Q value = 0.06

Table S67. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	36	16	16
subtype1	0	5	7	4
subtype2	2	19	0	6
subtype3	4	4	2	1
subtype4	3	6	7	3
subtype5	0	2	0	2

Figure S59. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.124 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	41	9	18
subtype1	0	6	4	6
subtype2	2	19	1	5
subtype3	4	4	1	2
subtype4	3	9	3	4
subtype5	0	3	0	1

Figure S60. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.292 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	67	10
subtype1	13	3
subtype2	25	2
subtype3	11	0
subtype4	15	4
subtype5	3	1

Figure S61. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.326 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	31
subtype1	7	10
subtype2	16	11
subtype3	9	3
subtype4	14	6
subtype5	3	1

Figure S62. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.105 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	67
subtype1	0	0	15
subtype2	0	0	23
subtype3	1	1	8
subtype4	0	0	18
subtype5	0	0	3

Figure S63. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.7 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	30
subtype1	3	4
subtype2	2	8
subtype3	1	6
subtype4	2	9
subtype5	0	3

Figure S64. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S73. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	29	22	27

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

P value = 0.00034 (logrank test), Q value = 0.027

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	28	4.1 - 153.6 (34.9)
subtype1	29	10	4.1 - 152.2 (31.8)
subtype2	22	14	5.2 - 153.6 (23.2)
subtype3	27	4	6.9 - 121.2 (43.3)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.412 (Kruskal-Wallis (anova)), Q value = 1

Table S75. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.8 (15.9)
subtype1	29	45.4 (14.5)
subtype2	22	44.2 (18.1)
subtype3	27	50.3 (15.3)

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

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

P value = 0.00059 (Fisher's exact test), Q value = 0.044

Table S76. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	35	16	16
subtype1	4	10	8	7
subtype2	0	7	5	9
subtype3	5	18	3	0

Figure S67. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 9e-04 (Fisher's exact test), Q value = 0.065

Table S77. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	40	9	18
subtype1	4	14	2	9
subtype2	0	8	4	9
subtype3	5	18	3	0

Figure S68. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.027 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	66	10
subtype1	23	6
subtype2	17	4
subtype3	26	0

Figure S69. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0634 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	21	8
subtype2	16	6
subtype3	12	15

Figure S70. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RACE'

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

Table S80. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	66
subtype1	1	1	25
subtype2	0	0	21
subtype3	0	0	20

Figure S71. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.865 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	29
subtype1	3	14
subtype2	4	10
subtype3	1	5

Figure S72. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S82. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	40	24	14

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

P value = 0.0715 (logrank test), Q value = 1

Table S83. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	78	28	4.1 - 153.6 (34.9)
subtype1	40	19	4.1 - 152.2 (34.4)
subtype2	24	5	6.9 - 153.6 (37.9)
subtype3	14	4	12.6 - 100.2 (30.2)

Figure S73. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.969 (Kruskal-Wallis (anova)), Q value = 1

Table S84. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	78	46.8 (15.9)
subtype1	40	46.5 (16.4)
subtype2	24	47.2 (15.4)
subtype3	14	46.6 (16.1)

Figure S74. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00348 (Fisher's exact test), Q value = 0.25

Table S85. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	9	35	16	16
subtype1	4	12	13	10
subtype2	2	18	0	4
subtype3	3	5	3	2

Figure S75. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.09 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	40	9	18
subtype1	4	17	5	13
subtype2	2	18	1	3
subtype3	3	5	3	2

Figure S76. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.156 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	66	10
subtype1	31	8
subtype2	22	2
subtype3	13	0

Figure S77. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.364 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	29
subtype1	28	12
subtype2	14	10
subtype3	7	7

Figure S78. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.408 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	1	66
subtype1	0	1	35
subtype2	0	0	20
subtype3	1	0	11

Figure S79. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S90. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	29
subtype1	5	18
subtype2	2	7
subtype3	1	4

Figure S80. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data

Input

Cluster data file = ACC-TP.mergedcluster.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 92
Number of clustering approaches = 10
Number of selected clinical features = 8
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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