Correlation between 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 8 different clustering approaches and 11 clinical features across 134 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'LYMPH.NODE.METASTASIS'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. 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 8 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.978 (1.00)	0.57 (1.00)	0.245 (1.00)	0.485 (1.00)	0.586 (1.00)	0.536 (1.00)	0.242 (1.00)	0.581 (1.00)
AGE	ANOVA	0.418 (1.00)	0.0565 (1.00)	0.473 (1.00)	0.227 (1.00)	0.357 (1.00)	0.358 (1.00)	0.083 (1.00)	0.112 (1.00)
GENDER	Chi-square test	1 (1.00)	0.055 (1.00)	0.633 (1.00)	0.166 (1.00)	0.79 (1.00)	0.3 (1.00)	0.0713 (1.00)	0.044 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.522 (1.00)	0.854 (1.00)	0.465 (1.00)	0.359 (1.00)	0.498 (1.00)	0.841 (1.00)	0.735 (1.00)	0.855 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.0268 (1.00)	0.996 (1.00)	0.657 (1.00)	0.943 (1.00)	0.586 (1.00)	0.317 (1.00)	0.452 (1.00)	0.262 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	ANOVA	0.597 (1.00)	0.509 (1.00)			0.845 (1.00)	0.538 (1.00)	0.508 (1.00)	0.256 (1.00)
DISTANT METASTASIS	Chi-square test	0.297 (1.00)	0.595 (1.00)			0.111 (1.00)	0.00829 (0.58)	0.315 (1.00)	0.984 (1.00)
LYMPH NODE METASTASIS	Chi-square test	0.0455 (1.00)	0.187 (1.00)			0.28 (1.00)	0.000632 (0.0455)	0.0222 (1.00)	0.0135 (0.929)
NUMBER OF LYMPH NODES	ANOVA	0.466 (1.00)	0.2 (1.00)	0.201 (1.00)	0.697 (1.00)	0.265 (1.00)	0.0193 (1.00)	0.327 (1.00)	0.0185 (1.00)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.322 (1.00)	0.135 (1.00)			0.424 (1.00)	0.00446 (0.317)	0.0352 (1.00)	0.0367 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	39	58	34

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

P value = 0.978 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	32	0.1 - 131.2 (7.0)
subtype1	37	10	0.8 - 100.5 (6.4)
subtype2	54	13	0.1 - 131.2 (7.0)
subtype3	33	9	0.4 - 123.8 (7.7)

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 = 0.418 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	130	67.4 (10.6)
subtype1	38	68.7 (10.9)
subtype2	58	66.0 (11.4)
subtype3	34	68.1 (8.7)

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 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	33	98
subtype1	10	29
subtype2	15	43
subtype3	8	26

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

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

P value = 0.522 (ANOVA), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	37	78.1 (16.3)
subtype1	13	82.3 (12.4)
subtype2	13	75.4 (20.7)
subtype3	11	76.4 (15.0)

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0268 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	81	36.6 (23.0)
subtype1	24	31.7 (24.1)
subtype2	32	32.4 (18.1)
subtype3	25	46.8 (25.1)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.597 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	80	2.8 (1.2)
subtype1	26	2.6 (1.2)
subtype2	32	2.8 (1.2)
subtype3	22	3.0 (1.0)

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

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

P value = 0.297 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	34	2	44
subtype1	7	0	18
subtype2	17	1	15
subtype3	10	1	11

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

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

P value = 0.0455 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3	NX
ALL	45	9	14	3	8
subtype1	8	4	6	1	5
subtype2	25	1	5	0	2
subtype3	12	4	3	2	1

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.466 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	98	1.9 (3.8)
subtype1	29	1.9 (2.9)
subtype2	42	1.4 (2.7)
subtype3	27	2.6 (5.6)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.322 (Chi-square test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	27	26
subtype1	9	6	10
subtype2	10	15	7
subtype3	7	6	9

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S12. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	34	46	33	21

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.57 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	127	33	0.1 - 131.2 (6.9)
subtype1	33	6	0.8 - 118.9 (6.7)
subtype2	45	15	0.4 - 131.2 (7.8)
subtype3	28	5	0.7 - 37.8 (6.4)
subtype4	21	7	0.1 - 46.8 (7.2)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0565 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	133	67.4 (10.5)
subtype1	33	67.8 (10.1)
subtype2	46	68.6 (9.6)
subtype3	33	63.3 (12.2)
subtype4	21	70.5 (9.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.055 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	34	100
subtype1	10	24
subtype2	16	30
subtype3	3	30
subtype4	5	16

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.854 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	7	82.9 (15.0)
subtype2	10	79.0 (15.2)
subtype3	16	76.2 (18.9)
subtype4	5	78.0 (13.0)

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.996 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	82	37.1 (23.2)
subtype1	22	36.7 (28.1)
subtype2	32	36.8 (21.2)
subtype3	21	38.2 (21.7)
subtype4	7	36.7 (24.9)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.509 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	80	2.8 (1.2)
subtype1	19	2.5 (1.2)
subtype2	27	2.9 (0.9)
subtype3	24	3.0 (1.4)
subtype4	10	2.7 (1.2)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.595 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	34	2	44
subtype1	8	0	10
subtype2	9	1	17
subtype3	12	0	12
subtype4	5	1	5

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.187 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	45	9	14	3	8
subtype1	5	3	7	1	2
subtype2	18	4	2	1	2
subtype3	17	2	2	1	2
subtype4	5	0	3	0	2

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.2 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	98	1.9 (3.8)
subtype1	24	2.2 (3.5)
subtype2	36	1.1 (2.4)
subtype3	24	1.7 (4.7)
subtype4	14	3.6 (4.8)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.135 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	27	26
subtype1	4	4	10
subtype2	6	13	7
subtype3	11	7	6
subtype4	5	3	3

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S23. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	12	10	14	17

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.245 (logrank test), Q value = 1

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	12	4	5.7 - 49.9 (10.9)
subtype2	10	5	1.8 - 100.5 (5.5)
subtype3	13	2	0.5 - 19.5 (6.6)
subtype4	17	9	0.4 - 118.9 (14.9)

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.473 (ANOVA), Q value = 1

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	12	63.9 (9.2)
subtype2	10	69.5 (9.3)
subtype3	13	66.5 (10.2)
subtype4	17	69.1 (10.3)

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.633 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	21	32
subtype1	6	6
subtype2	5	5
subtype3	5	9
subtype4	5	12

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

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

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

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	5	88.0 (4.5)
subtype2	1	70.0 (NA)
subtype3	1	60.0 (NA)
subtype4	4	77.5 (25.0)

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.657 (ANOVA), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	27	32.1 (17.5)
subtype1	9	27.6 (12.4)
subtype2	5	28.8 (18.4)
subtype3	6	35.2 (20.4)
subtype4	7	37.9 (21.6)

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'TUMOR.STAGECODE'

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

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGECODE'

	nPatients	Mean (Std.Dev)
ALL	0	NaN (NA)

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S30. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	14	15	24

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.485 (logrank test), Q value = 1

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	14	9	0.4 - 118.9 (10.4)
subtype2	15	5	1.9 - 49.9 (8.3)
subtype3	23	6	0.5 - 100.5 (6.6)

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.227 (ANOVA), Q value = 1

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	14	70.5 (9.0)
subtype2	15	64.2 (9.6)
subtype3	23	67.4 (10.2)

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.166 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	32
subtype1	5	9
subtype2	9	6
subtype3	7	17

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.359 (ANOVA), Q value = 1

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	3	73.3 (28.9)
subtype2	4	90.0 (0.0)
subtype3	4	75.0 (12.9)

Figure S29. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	27	32.1 (17.5)
subtype1	5	33.0 (23.3)
subtype2	9	30.4 (15.6)
subtype3	13	33.0 (17.8)

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGECODE'

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

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGECODE'

	nPatients	Mean (Std.Dev)
ALL	0	NaN (NA)

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	45	21	41	24

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.586 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	32	0.1 - 131.2 (7.0)
subtype1	40	7	0.1 - 100.5 (6.6)
subtype2	21	7	0.8 - 118.9 (6.6)
subtype3	40	12	0.4 - 75.3 (7.8)
subtype4	23	6	1.3 - 131.2 (6.8)

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

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

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

	nPatients	Mean (Std.Dev)
ALL	130	67.2 (10.6)
subtype1	44	67.0 (11.9)
subtype2	21	64.9 (9.3)
subtype3	41	66.8 (10.6)
subtype4	24	70.4 (8.9)

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.79 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	34	97
subtype1	10	35
subtype2	5	16
subtype3	13	28
subtype4	6	18

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.498 (ANOVA), Q value = 1

Table S41. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	17	77.1 (17.2)
subtype2	7	85.7 (7.9)
subtype3	12	78.3 (18.5)
subtype4	2	65.0 (7.1)

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.586 (ANOVA), Q value = 1

Table S42. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	80	36.6 (23.1)
subtype1	22	42.0 (30.9)
subtype2	18	35.1 (21.1)
subtype3	28	33.0 (17.9)
subtype4	12	37.2 (20.9)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.845 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	79	2.8 (1.2)
subtype1	25	2.7 (1.3)
subtype2	13	2.7 (1.1)
subtype3	25	3.0 (1.0)
subtype4	16	2.7 (1.2)

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.111 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	33	2	44
subtype1	14	0	12
subtype2	4	0	8
subtype3	9	0	16
subtype4	6	2	8

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.28 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3	NX
ALL	44	9	14	3	8
subtype1	13	2	7	0	3
subtype2	6	1	2	1	2
subtype3	16	5	0	1	3
subtype4	9	1	5	1	0

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.265 (ANOVA), Q value = 1

Table S46. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	97	1.9 (3.8)
subtype1	29	2.1 (3.2)
subtype2	14	0.6 (1.0)
subtype3	32	1.5 (4.3)
subtype4	22	3.0 (4.5)

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.424 (Chi-square test), Q value = 1

Table S47. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	26	26
subtype1	12	5	8
subtype2	4	5	4
subtype3	7	11	7
subtype4	3	5	7

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S48. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	29	45	57

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.536 (logrank test), Q value = 1

Table S49. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	32	0.1 - 131.2 (7.0)
subtype1	29	9	0.5 - 100.5 (5.8)
subtype2	39	6	0.1 - 37.8 (6.6)
subtype3	56	17	0.4 - 131.2 (7.8)

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 = 0.358 (ANOVA), Q value = 1

Table S50. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	67.2 (10.6)
subtype1	29	69.7 (9.5)
subtype2	44	66.1 (11.4)
subtype3	57	66.9 (10.4)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

Table S51. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	97
subtype1	8	21
subtype2	8	37
subtype3	18	39

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

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

P value = 0.841 (ANOVA), Q value = 1

Table S52. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	6	81.7 (13.3)
subtype2	17	77.1 (17.2)
subtype3	15	78.7 (16.8)

Figure S44. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.317 (ANOVA), Q value = 1

Table S53. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	80	36.6 (23.1)
subtype1	13	44.8 (30.1)
subtype2	27	37.1 (25.3)
subtype3	40	33.6 (18.6)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.538 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	79	2.8 (1.2)
subtype1	12	2.8 (1.1)
subtype2	32	2.6 (1.3)
subtype3	35	2.9 (1.0)

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

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00829 (Chi-square test), Q value = 0.58

Table S55. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	33	2	44
subtype1	3	2	8
subtype2	17	0	14
subtype3	13	0	22

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

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

P value = 0.000632 (Chi-square test), Q value = 0.045

Table S56. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	44	9	14	3	8
subtype1	4	1	8	0	0
subtype2	16	4	5	0	5
subtype3	24	4	1	3	3

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

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0193 (ANOVA), Q value = 1

Table S57. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	97	1.9 (3.8)
subtype1	23	3.8 (4.3)
subtype2	28	1.6 (3.1)
subtype3	46	1.2 (3.7)

Figure S49. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00446 (Chi-square test), Q value = 0.32

Table S58. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	26	26
subtype1	1	2	9
subtype2	15	8	8
subtype3	10	16	9

Figure S50. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S59. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	32	54	48

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.242 (logrank test), Q value = 1

Table S60. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	127	33	0.1 - 131.2 (6.9)
subtype1	28	3	0.8 - 118.9 (5.4)
subtype2	51	12	0.4 - 49.9 (7.0)
subtype3	48	18	0.1 - 131.2 (8.2)

Figure S51. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.083 (ANOVA), Q value = 1

Table S61. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	133	67.4 (10.5)
subtype1	31	69.8 (11.3)
subtype2	54	65.0 (10.6)
subtype3	48	68.5 (9.6)

Figure S52. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0713 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	100
subtype1	4	28
subtype2	13	41
subtype3	17	31

Figure S53. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.735 (ANOVA), Q value = 1

Table S63. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	7	82.9 (9.5)
subtype2	24	77.5 (18.9)
subtype3	7	77.1 (11.1)

Figure S54. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.452 (ANOVA), Q value = 1

Table S64. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	82	37.1 (23.2)
subtype1	20	37.5 (29.2)
subtype2	37	33.9 (17.7)
subtype3	25	41.5 (25.3)

Figure S55. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.508 (ANOVA), Q value = 1

Table S65. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	80	2.8 (1.2)
subtype1	20	2.5 (1.4)
subtype2	36	2.8 (1.0)
subtype3	24	3.0 (1.1)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.315 (Chi-square test), Q value = 1

Table S66. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	34	2	44
subtype1	8	0	11
subtype2	15	0	21
subtype3	11	2	12

Figure S57. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.0222 (Chi-square test), Q value = 1

Table S67. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	45	9	14	3	8
subtype1	8	5	4	2	0
subtype2	21	4	3	1	6
subtype3	16	0	7	0	2

Figure S58. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.327 (ANOVA), Q value = 1

Table S68. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	98	1.9 (3.8)
subtype1	24	2.5 (5.0)
subtype2	36	1.1 (2.7)
subtype3	38	2.2 (3.7)

Figure S59. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0352 (Chi-square test), Q value = 1

Table S69. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	27	26
subtype1	3	4	11
subtype2	16	12	8
subtype3	7	11	7

Figure S60. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S70. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	20	63	7	36	8

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.581 (logrank test), Q value = 1

Table S71. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	127	33	0.1 - 131.2 (6.9)
subtype1	16	3	1.5 - 118.9 (5.9)
subtype2	60	19	0.1 - 49.9 (7.2)
subtype3	7	1	0.8 - 7.9 (4.1)
subtype4	36	8	0.5 - 131.2 (8.5)
subtype5	8	2	1.4 - 75.3 (6.2)

Figure S61. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.112 (ANOVA), Q value = 1

Table S72. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	133	67.4 (10.5)
subtype1	19	68.4 (12.3)
subtype2	63	65.0 (10.3)
subtype3	7	68.0 (7.2)
subtype4	36	69.5 (10.3)
subtype5	8	73.4 (8.3)

Figure S62. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.044 (Chi-square test), Q value = 1

Table S73. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	100
subtype1	3	17
subtype2	17	46
subtype3	0	7
subtype4	14	22
subtype5	0	8

Figure S63. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.855 (ANOVA), Q value = 1

Table S74. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	5	82.0 (11.0)
subtype2	26	77.3 (18.2)
subtype3	2	85.0 (7.1)
subtype4	4	77.5 (15.0)
subtype5	1	80.0 (NA)

Figure S64. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.262 (ANOVA), Q value = 1

Table S75. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	82	37.1 (23.2)
subtype1	12	37.5 (25.8)
subtype2	41	37.2 (20.4)
subtype3	5	53.2 (37.0)
subtype4	17	38.1 (24.1)
subtype5	7	22.3 (18.9)

Figure S65. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.256 (ANOVA), Q value = 1

Table S76. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	80	2.8 (1.2)
subtype1	11	2.4 (1.4)
subtype2	38	2.8 (1.1)
subtype3	7	2.6 (1.4)
subtype4	18	2.7 (1.1)
subtype5	6	3.7 (0.8)

Figure S66. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.984 (Chi-square test), Q value = 1

Table S77. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	34	2	44
subtype1	4	0	7
subtype2	17	1	20
subtype3	2	0	4
subtype4	8	1	10
subtype5	3	0	3

Figure S67. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.0135 (Chi-square test), Q value = 0.93

Table S78. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	45	9	14	3	8
subtype1	4	3	3	1	0
subtype2	22	3	3	1	8
subtype3	2	2	1	1	0
subtype4	12	0	7	0	0
subtype5	5	1	0	0	0

Figure S68. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0185 (ANOVA), Q value = 1

Table S79. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	98	1.9 (3.8)
subtype1	14	1.6 (3.2)
subtype2	40	1.0 (2.4)
subtype3	5	6.4 (9.3)
subtype4	31	2.7 (4.1)
subtype5	8	0.8 (1.4)

Figure S69. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0367 (Chi-square test), Q value = 1

Table S80. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III	STAGE IV
ALL	26	27	26
subtype1	2	1	6
subtype2	19	12	7
subtype3	1	2	4
subtype4	3	9	7
subtype5	1	3	2

Figure S70. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = BLCA-TP.mergedcluster.txt
Clinical data file = BLCA-TP.clin.merged.picked.txt
Number of patients = 134
Number of clustering approaches = 8
Number of selected clinical features = 11
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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