Correlation between aggregated molecular cancer subtypes and selected clinical features

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 12 different clustering approaches and 8 clinical features across 502 patients, 33 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death' and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 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, 33 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	DISTANT METASTASIS	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA	Fisher's exact test	Chi-square test	ANOVA	Chi-square test
mRNA CNMF subtypes	0.231 (1.00)	0.795 (1.00)	0.634 (1.00)		0.12 (1.00)	0.171 (1.00)		0.00527 (0.258)
mRNA cHierClus subtypes	0.588 (1.00)	0.651 (1.00)	0.705 (1.00)		0.104 (1.00)	0.172 (1.00)		0.00728 (0.342)
Copy Number Ratio CNMF subtypes	0.000614 (0.0369)	0.015 (0.632)	0.00221 (0.117)	0.29 (1.00)	0.00062 (0.0369)	0.1 (1.00)		0.00027 (0.017)
METHLYATION CNMF	4.7e-06 (0.000329)	0.00373 (0.19)	0.00031 (0.0192)	0.703 (1.00)	0.000191 (0.0122)	0.426 (1.00)		4.14e-11 (3.35e-09)
RPPA CNMF subtypes	1.79e-09 (1.43e-07)	0.129 (1.00)	0.0611 (1.00)	0.322 (1.00)	3.89e-07 (2.91e-05)	0.00083 (0.0465)		7.63e-09 (6.03e-07)
RPPA cHierClus subtypes	8.92e-08 (6.87e-06)	0.00927 (0.408)	0.578 (1.00)	0.0463 (1.00)	9.01e-05 (0.00586)	0.00271 (0.141)		1.42e-06 (0.000105)
RNAseq CNMF subtypes	1.63e-06 (0.000119)	0.09 (1.00)	6.19e-05 (0.00414)	0.66 (1.00)	0.000645 (0.0374)	0.0564 (1.00)		3.71e-07 (2.82e-05)
RNAseq cHierClus subtypes	3.34e-08 (2.6e-06)	0.358 (1.00)	0.00158 (0.0869)	0.357 (1.00)	2e-06 (0.000144)	0.00482 (0.241)		7.38e-13 (6.05e-11)
MIRSEQ CNMF	2.31e-06 (0.000164)	0.0424 (1.00)	0.000437 (0.0267)	0.172 (1.00)	5.25e-05 (0.00357)	0.0155 (0.636)		2.95e-05 (0.00204)
MIRSEQ CHIERARCHICAL	0.00211 (0.114)	0.298 (1.00)	0.124 (1.00)	0.943 (1.00)	0.351 (1.00)	0.0166 (0.665)		0.000659 (0.0376)
MIRseq Mature CNMF subtypes	7.54e-05 (0.00498)	0.0809 (1.00)	0.01 (0.43)	0.465 (1.00)	0.00738 (0.342)	0.275 (1.00)		0.00781 (0.352)
MIRseq Mature cHierClus subtypes	0.00711 (0.341)	0.1 (1.00)	0.0406 (1.00)	0.601 (1.00)	0.0392 (1.00)	0.0474 (1.00)		0.169 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	34	24	14

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.231 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	71	13	0.5 - 101.1 (32.6)
subtype1	33	4	0.5 - 101.1 (31.0)
subtype2	24	8	0.5 - 93.3 (36.7)
subtype3	14	1	1.3 - 84.4 (25.0)

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 = 0.795 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	33	60.2 (13.8)
subtype2	24	59.9 (11.1)
subtype3	14	62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	29	43
subtype1	15	19
subtype2	10	14
subtype3	4	10

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

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	67	5
subtype1	33	1
subtype2	20	4
subtype3	14	0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.171 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	35	3	34
subtype1	18	0	16
subtype2	10	3	11
subtype3	7	0	7

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00527 (Chi-square test), Q value = 0.26

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	40	13	14	5
subtype1	23	4	6	1
subtype2	9	3	8	4
subtype3	8	6	0	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	15	33	24

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.588 (logrank test), Q value = 1

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	71	13	0.5 - 101.1 (32.6)
subtype1	15	2	1.3 - 84.4 (24.2)
subtype2	32	4	0.5 - 101.1 (30.5)
subtype3	24	7	0.5 - 93.3 (37.0)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.651 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	15	63.2 (11.2)
subtype2	32	59.9 (14.0)
subtype3	24	59.7 (10.9)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.705 (Fisher's exact test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	43
subtype1	5	10
subtype2	15	18
subtype3	9	15

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

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.104 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1
ALL	67	5
subtype1	15	0
subtype2	32	1
subtype3	20	4

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

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

P value = 0.172 (Chi-square test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	35	3	34
subtype1	7	0	8
subtype2	17	0	16
subtype3	11	3	10

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00728 (Chi-square test), Q value = 0.34

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	40	13	14	5
subtype1	9	6	0	0
subtype2	22	4	6	1
subtype3	9	3	8	4

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

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

Table S15. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	161	210	122

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

P value = 0.000614 (logrank test), Q value = 0.037

Table S16. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	490	158	0.1 - 111.0 (35.2)
subtype1	161	69	0.1 - 109.9 (31.3)
subtype2	209	48	0.1 - 111.0 (37.0)
subtype3	120	41	0.1 - 91.4 (35.7)

Figure S13. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.015 (ANOVA), Q value = 0.63

Table S17. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	492	60.6 (12.2)
subtype1	160	62.8 (11.7)
subtype2	210	59.4 (12.5)
subtype3	122	59.6 (11.9)

Figure S14. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00221 (Fisher's exact test), Q value = 0.12

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	171	322
subtype1	39	122
subtype2	86	124
subtype3	46	76

Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.29 (ANOVA), Q value = 1

Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	36	88.3 (23.0)
subtype1	15	81.3 (33.6)
subtype2	12	91.7 (9.4)
subtype3	9	95.6 (7.3)

Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.00062 (Fisher's exact test), Q value = 0.037

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	417	76
subtype1	123	38
subtype2	191	19
subtype3	103	19

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.1 (Chi-square test), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	228	18	247
subtype1	72	11	78
subtype2	101	3	106
subtype3	55	4	63

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.00027 (Chi-square test), Q value = 0.017

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	237	53	126	77
subtype1	57	17	49	38
subtype2	122	22	48	18
subtype3	58	14	29	21

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S23. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	96	118	69

'METHLYATION CNMF' versus 'Time to Death'

P value = 4.7e-06 (logrank test), Q value = 0.00033

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	281	95	0.1 - 109.9 (28.5)
subtype1	95	48	0.1 - 84.7 (28.8)
subtype2	117	20	0.2 - 109.6 (31.5)
subtype3	69	27	0.1 - 109.9 (20.4)

Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00373 (ANOVA), Q value = 0.19

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	283	61.5 (12.0)
subtype1	96	63.9 (10.6)
subtype2	118	58.7 (12.7)
subtype3	69	62.8 (11.7)

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00031 (Fisher's exact test), Q value = 0.019

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	187
subtype1	20	76
subtype2	55	63
subtype3	21	48

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.703 (ANOVA), Q value = 1

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	92.5 (8.0)
subtype1	6	91.7 (7.5)
subtype2	16	91.9 (8.3)
subtype3	6	95.0 (8.4)

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.000191 (Fisher's exact test), Q value = 0.012

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	232	51
subtype1	67	29
subtype2	108	10
subtype3	57	12

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.426 (Chi-square test), Q value = 1

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	127	9	147
subtype1	41	5	50
subtype2	54	1	63
subtype3	32	3	34

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.14e-11 (Chi-square test), Q value = 3.3e-09

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	129	27	74	53
subtype1	19	9	38	30
subtype2	79	15	15	9
subtype3	31	3	21	14

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	101	90	86	76	44	57

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.79e-09 (logrank test), Q value = 1.4e-07

Table S32. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	452	151	0.1 - 111.0 (34.3)
subtype1	101	26	0.2 - 111.0 (43.7)
subtype2	90	35	0.1 - 90.4 (29.4)
subtype3	85	22	0.2 - 93.0 (35.3)
subtype4	75	23	0.1 - 96.8 (27.9)
subtype5	44	8	0.2 - 83.8 (36.5)
subtype6	57	37	0.6 - 84.0 (19.7)

Figure S27. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

Table S33. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	453	60.4 (12.3)
subtype1	100	61.8 (11.1)
subtype2	90	58.0 (12.2)
subtype3	86	62.3 (12.6)
subtype4	76	60.0 (11.8)
subtype5	44	58.3 (15.7)
subtype6	57	61.2 (11.4)

Figure S28. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0611 (Chi-square test), Q value = 1

Table S34. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	151	303
subtype1	44	57
subtype2	29	61
subtype3	27	59
subtype4	18	58
subtype5	18	26
subtype6	15	42

Figure S29. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.322 (ANOVA), Q value = 1

Table S35. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	93.5 (7.7)
subtype1	7	92.9 (7.6)
subtype2	8	93.8 (5.2)
subtype3	10	90.0 (9.4)
subtype4	2	100.0 (0.0)
subtype5	4	100.0 (0.0)
subtype6	3	93.3 (11.5)

Figure S30. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 3.89e-07 (Chi-square test), Q value = 2.9e-05

Table S36. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	380	74
subtype1	87	14
subtype2	71	19
subtype3	76	10
subtype4	68	8
subtype5	44	0
subtype6	34	23

Figure S31. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.00083 (Chi-square test), Q value = 0.046

Table S37. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	208	16	230
subtype1	48	1	52
subtype2	46	2	42
subtype3	43	2	41
subtype4	35	3	38
subtype5	13	0	31
subtype6	23	8	26

Figure S32. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.63e-09 (Chi-square test), Q value = 6e-07

Table S38. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	219	44	116	75
subtype1	54	11	22	14
subtype2	42	9	19	20
subtype3	39	12	25	10
subtype4	44	7	17	8
subtype5	33	2	9	0
subtype6	7	3	24	23

Figure S33. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S39. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	189	153	112

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 8.92e-08 (logrank test), Q value = 6.9e-06

Table S40. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	452	151	0.1 - 111.0 (34.3)
subtype1	189	42	0.1 - 96.8 (37.0)
subtype2	153	50	0.2 - 111.0 (36.8)
subtype3	110	59	0.1 - 91.4 (21.5)

Figure S34. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA), Q value = 0.41

Table S41. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	453	60.4 (12.3)
subtype1	189	58.4 (12.7)
subtype2	152	62.3 (12.3)
subtype3	112	61.3 (11.1)

Figure S35. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	151	303
subtype1	68	121
subtype2	47	106
subtype3	36	76

Figure S36. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0463 (ANOVA), Q value = 1

Table S43. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	93.5 (7.7)
subtype1	17	96.5 (4.9)
subtype2	10	89.0 (8.8)
subtype3	7	92.9 (9.5)

Figure S37. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 9.01e-05 (Fisher's exact test), Q value = 0.0059

Table S44. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	380	74
subtype1	170	19
subtype2	131	22
subtype3	79	33

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

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

P value = 0.00271 (Chi-square test), Q value = 0.14

Table S45. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	208	16	230
subtype1	72	4	113
subtype2	76	4	73
subtype3	60	8	44

Figure S39. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.42e-06 (Chi-square test), Q value = 1e-04

Table S46. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	219	44	116	75
subtype1	118	16	36	19
subtype2	65	17	48	23
subtype3	36	11	32	33

Figure S40. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S47. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	199	180	101

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.63e-06 (logrank test), Q value = 0.00012

Table S48. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	478	155	0.1 - 111.0 (34.3)
subtype1	199	44	0.1 - 111.0 (37.0)
subtype2	179	83	0.1 - 90.3 (30.6)
subtype3	100	28	0.1 - 93.3 (35.2)

Figure S41. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.09 (ANOVA), Q value = 1

Table S49. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	479	60.6 (12.2)
subtype1	198	61.7 (12.2)
subtype2	180	60.6 (11.8)
subtype3	101	58.4 (12.7)

Figure S42. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 6.19e-05 (Fisher's exact test), Q value = 0.0041

Table S50. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	167	313
subtype1	91	108
subtype2	44	136
subtype3	32	69

Figure S43. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.66 (ANOVA), Q value = 1

Table S51. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	90.9 (17.8)
subtype1	14	91.4 (8.6)
subtype2	12	87.5 (28.3)
subtype3	8	95.0 (7.6)

Figure S44. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.000645 (Fisher's exact test), Q value = 0.037

Table S52. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	403	77
subtype1	176	23
subtype2	136	44
subtype3	91	10

Figure S45. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0564 (Chi-square test), Q value = 1

Table S53. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	228	17	235
subtype1	96	2	101
subtype2	85	12	83
subtype3	47	3	51

Figure S46. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 3.71e-07 (Chi-square test), Q value = 2.8e-05

Table S54. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	233	49	120	78
subtype1	118	19	39	23
subtype2	55	20	61	44
subtype3	60	10	20	11

Figure S47. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S55. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	73	215	192

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.34e-08 (logrank test), Q value = 2.6e-06

Table S56. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	478	155	0.1 - 111.0 (34.3)
subtype1	72	14	0.2 - 92.0 (27.8)
subtype2	214	48	0.1 - 111.0 (37.2)
subtype3	192	93	0.1 - 93.3 (30.5)

Figure S48. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.358 (ANOVA), Q value = 1

Table S57. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	479	60.6 (12.2)
subtype1	73	58.7 (13.2)
subtype2	214	61.0 (12.4)
subtype3	192	60.8 (11.5)

Figure S49. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00158 (Fisher's exact test), Q value = 0.087

Table S58. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	167	313
subtype1	23	50
subtype2	93	122
subtype3	51	141

Figure S50. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.357 (ANOVA), Q value = 1

Table S59. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	90.9 (17.8)
subtype1	10	95.0 (9.7)
subtype2	13	93.1 (6.3)
subtype3	11	84.5 (29.1)

Figure S51. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 2e-06 (Fisher's exact test), Q value = 0.00014

Table S60. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	403	77
subtype1	70	3
subtype2	191	24
subtype3	142	50

Figure S52. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.00482 (Chi-square test), Q value = 0.24

Table S61. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	228	17	235
subtype1	32	2	39
subtype2	104	1	110
subtype3	92	14	86

Figure S53. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.38e-13 (Chi-square test), Q value = 6.1e-11

Table S62. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	233	49	120	78
subtype1	54	6	10	3
subtype2	126	22	44	23
subtype3	53	21	66	52

Figure S54. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S63. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	117	192	172

'MIRSEQ CNMF' versus 'Time to Death'

P value = 2.31e-06 (logrank test), Q value = 0.00016

Table S64. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	479	156	0.1 - 111.0 (35.2)
subtype1	117	32	0.1 - 109.9 (37.0)
subtype2	192	43	0.1 - 111.0 (35.8)
subtype3	170	81	0.2 - 93.3 (30.6)

Figure S55. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0424 (ANOVA), Q value = 1

Table S65. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	481	60.6 (12.2)
subtype1	117	58.6 (12.3)
subtype2	192	62.1 (12.2)
subtype3	172	60.2 (11.9)

Figure S56. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.000437 (Fisher's exact test), Q value = 0.027

Table S66. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	163	318
subtype1	37	80
subtype2	84	108
subtype3	42	130

Figure S57. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.172 (ANOVA), Q value = 1

Table S67. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	36	88.3 (23.0)
subtype1	9	95.6 (7.3)
subtype2	15	92.0 (8.6)
subtype3	12	78.3 (37.1)

Figure S58. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 5.25e-05 (Fisher's exact test), Q value = 0.0036

Table S68. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	405	76
subtype1	108	9
subtype2	169	23
subtype3	128	44

Figure S59. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0155 (Chi-square test), Q value = 0.64

Table S69. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	222	18	241
subtype1	56	3	58
subtype2	86	2	104
subtype3	80	13	79

Figure S60. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.95e-05 (Chi-square test), Q value = 0.002

Table S70. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	228	51	125	77
subtype1	66	11	30	10
subtype2	106	18	45	23
subtype3	56	22	50	44

Figure S61. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S71. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	37	162	282

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00211 (logrank test), Q value = 0.11

Table S72. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	479	156	0.1 - 111.0 (35.2)
subtype1	36	13	0.5 - 85.2 (29.0)
subtype2	162	70	0.1 - 109.9 (35.4)
subtype3	281	73	0.1 - 111.0 (35.2)

Figure S62. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.298 (ANOVA), Q value = 1

Table S73. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	481	60.6 (12.2)
subtype1	37	57.8 (11.0)
subtype2	162	60.3 (12.1)
subtype3	282	61.1 (12.4)

Figure S63. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S74. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	163	318
subtype1	13	24
subtype2	45	117
subtype3	105	177

Figure S64. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.943 (ANOVA), Q value = 1

Table S75. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	36	88.3 (23.0)
subtype1	2	95.0 (7.1)
subtype2	12	87.5 (28.3)
subtype3	22	88.2 (21.3)

Figure S65. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.351 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	405	76
subtype1	30	7
subtype2	132	30
subtype3	243	39

Figure S66. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0166 (Chi-square test), Q value = 0.67

Table S77. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	222	18	241
subtype1	19	3	15
subtype2	76	11	75
subtype3	127	4	151

Figure S67. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000659 (Chi-square test), Q value = 0.038

Table S78. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	228	51	125	77
subtype1	16	9	5	7
subtype2	60	15	56	31
subtype3	152	27	64	39

Figure S68. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S79. Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	51	90	76

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

P value = 7.54e-05 (logrank test), Q value = 0.005

Table S80. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	216	66	0.2 - 109.6 (36.7)
subtype1	51	12	3.9 - 91.4 (36.4)
subtype2	90	17	0.2 - 109.6 (40.8)
subtype3	75	37	0.2 - 87.5 (31.3)

Figure S69. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0809 (ANOVA), Q value = 1

Table S81. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	217	59.4 (12.7)
subtype1	51	56.1 (12.0)
subtype2	90	61.1 (12.8)
subtype3	76	59.5 (12.9)

Figure S70. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.01 (Fisher's exact test), Q value = 0.43

Table S82. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	65	152
subtype1	15	36
subtype2	36	54
subtype3	14	62

Figure S71. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.465 (ANOVA), Q value = 1

Table S83. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	30	93.3 (8.0)
subtype1	8	95.0 (7.6)
subtype2	11	90.9 (9.4)
subtype3	11	94.5 (6.9)

Figure S72. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00738 (Fisher's exact test), Q value = 0.34

Table S84. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	179	38
subtype1	46	5
subtype2	79	11
subtype3	54	22

Figure S73. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.275 (Chi-square test), Q value = 1

Table S85. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	96	8	113
subtype1	19	2	30
subtype2	41	1	48
subtype3	36	5	35

Figure S74. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.00781 (Chi-square test), Q value = 0.35

Table S86. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	105	23	48	41
subtype1	34	3	8	6
subtype2	45	11	22	12
subtype3	26	9	18	23

Figure S75. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S87. Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	59	93	65

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

P value = 0.00711 (logrank test), Q value = 0.34

Table S88. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	216	66	0.2 - 109.6 (36.7)
subtype1	58	26	0.2 - 92.0 (38.9)
subtype2	93	19	0.2 - 109.6 (43.0)
subtype3	65	21	0.2 - 91.4 (29.9)

Figure S76. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.1 (ANOVA), Q value = 1

Table S89. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	217	59.4 (12.7)
subtype1	59	59.6 (12.5)
subtype2	93	61.1 (12.4)
subtype3	65	56.7 (13.2)

Figure S77. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0406 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	65	152
subtype1	12	47
subtype2	36	57
subtype3	17	48

Figure S78. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.601 (ANOVA), Q value = 1

Table S91. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	30	93.3 (8.0)
subtype1	8	91.2 (11.3)
subtype2	10	93.0 (6.7)
subtype3	12	95.0 (6.7)

Figure S79. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0392 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	179	38
subtype1	42	17
subtype2	81	12
subtype3	56	9

Figure S80. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0474 (Chi-square test), Q value = 1

Table S93. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	96	8	113
subtype1	24	1	34
subtype2	41	1	51
subtype3	31	6	28

Figure S81. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.169 (Chi-square test), Q value = 1

Table S94. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	105	23	48	41
subtype1	24	4	13	18
subtype2	47	13	21	12
subtype3	34	6	14	11

Figure S82. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = KIRC-TP.mergedcluster.txt
Clinical data file = KIRC-TP.clin.merged.picked.txt
Number of patients = 502
Number of clustering approaches = 12
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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Fisher's exact test

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

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

References

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

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

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

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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