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 8 different clustering approaches and 9 clinical features across 127 patients, 25 significant findings 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 correlate to 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 25 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.528 (1.00)	0.393 (1.00)	0.638 (1.00)	0.666 (1.00)	0.413 (1.00)	0.237 (1.00)	0.455 (1.00)	0.16 (1.00)
AGE	Kruskal-Wallis (anova)	0.0216 (0.952)	0.000234 (0.0129)	0.000228 (0.0128)	0.0017 (0.0817)	9.65e-06 (0.000695)	0.000158 (0.00918)	2.45e-05 (0.00154)	0.000356 (0.0192)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.324 (1.00)	0.00015 (0.00885)	0.022 (0.952)	0.0228 (0.959)	5e-05 (0.0031)	0.0015 (0.0735)	0.00066 (0.0343)	5e-05 (0.0031)
PATHOLOGY T STAGE	Fisher's exact test	0.0676 (1.00)	0.00036 (0.0192)	0.0168 (0.757)	0.00688 (0.323)	0.00142 (0.071)	0.00017 (0.00969)	0.00092 (0.0469)	6e-05 (0.0036)
PATHOLOGY N STAGE	Fisher's exact test	0.466 (1.00)	0.263 (1.00)	0.0681 (1.00)	0.251 (1.00)	0.129 (1.00)	0.184 (1.00)	0.135 (1.00)	0.0261 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.174 (1.00)	0.157 (1.00)	0.0875 (1.00)	0.127 (1.00)	0.0507 (1.00)	0.0805 (1.00)	0.0354 (1.00)	0.0131 (0.604)
GENDER	Fisher's exact test	0.0979 (1.00)	0.846 (1.00)	0.0892 (1.00)	0.138 (1.00)	0.587 (1.00)	0.55 (1.00)	0.3 (1.00)	0.322 (1.00)
NUMBERPACKYEARSSMOKED	Kruskal-Wallis (anova)	0.042 (1.00)	0.36 (1.00)	0.602 (1.00)	0.675 (1.00)	0.318 (1.00)	0.698 (1.00)	0.132 (1.00)	0.251 (1.00)
RACE	Fisher's exact test	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)	1e-05 (0.00071)

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
Number of samples	37	36	32

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

P value = 0.528 (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	98	31	0.0 - 122.1 (4.2)
subtype1	35	12	0.1 - 122.1 (7.0)
subtype2	32	6	0.0 - 44.8 (0.2)
subtype3	31	13	0.1 - 60.4 (5.1)

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 = 0.0216 (Kruskal-Wallis (anova)), Q value = 0.95

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

	nPatients	Mean (Std.Dev)
ALL	105	62.6 (12.6)
subtype1	37	67.1 (11.8)
subtype2	36	59.1 (11.3)
subtype3	32	61.5 (13.6)

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 = 0.324 (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 IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	7	4	4	1	30	16	11	8	6	3	1
subtype1	4	2	1	0	10	4	2	4	1	2	0
subtype2	0	1	2	1	16	7	5	2	2	0	0
subtype3	3	1	1	0	4	5	4	2	3	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 = 0.0676 (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	T0+T1	T2	T3+T4
ALL	19	26	50
subtype1	9	5	18
subtype2	3	15	18
subtype3	7	6	14

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 = 0.466 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	50	33	8	3
subtype1	18	9	2	2
subtype2	21	13	2	0
subtype3	11	11	4	1

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 'PATHOLOGY.M.STAGE'

P value = 0.174 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1A	MX
ALL	75	1	15
subtype1	25	0	6
subtype2	32	0	3
subtype3	18	1	6

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0979 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	15	90
subtype1	9	28
subtype2	4	32
subtype3	2	30

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.042 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	63	36.5 (19.6)
subtype1	22	43.8 (22.4)
subtype2	23	35.2 (18.4)
subtype3	18	29.3 (14.6)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	WHITE
ALL	35	66
subtype1	5	29
subtype2	24	12
subtype3	6	25

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S11. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	55	24	48

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.393 (logrank test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	38	0.0 - 122.1 (5.4)
subtype1	55	19	0.3 - 122.1 (8.0)
subtype2	22	7	0.0 - 83.2 (4.8)
subtype3	42	12	0.1 - 53.9 (0.5)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000234 (Kruskal-Wallis (anova)), Q value = 0.013

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	127	63.6 (12.7)
subtype1	55	67.5 (11.8)
subtype2	24	65.6 (15.2)
subtype3	48	58.2 (10.5)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00015 (Fisher's exact test), Q value = 0.0088

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	7	4	5	1	34	22	16	8	6	4	2	1
subtype1	7	2	2	0	3	11	7	1	3	3	2	1
subtype2	0	1	2	0	7	3	3	4	0	1	0	0
subtype3	0	1	1	1	24	8	6	3	3	0	0	0

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

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

P value = 0.00036 (Fisher's exact test), Q value = 0.019

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	21	30	63
subtype1	17	6	22
subtype2	1	7	14
subtype3	3	17	27

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

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

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	59	41	8	4
subtype1	19	20	3	3
subtype2	10	7	2	1
subtype3	30	14	3	0

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

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

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	90	1	2	17
subtype1	30	1	2	9
subtype2	18	0	0	4
subtype3	42	0	0	4

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.846 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	109
subtype1	9	46
subtype2	3	21
subtype3	6	42

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.36 (Kruskal-Wallis (anova)), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	75	36.7 (21.2)
subtype1	32	40.5 (23.9)
subtype2	15	36.8 (19.5)
subtype3	28	32.3 (18.6)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	36	2	84
subtype1	0	0	51
subtype2	7	0	16
subtype3	29	2	17

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	13	23	6	24

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.638 (logrank test), Q value = 1

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	64	20	0.0 - 33.3 (3.2)
subtype1	12	5	0.1 - 33.3 (0.3)
subtype2	22	4	0.1 - 20.1 (0.1)
subtype3	6	1	0.0 - 20.1 (2.0)
subtype4	24	10	0.3 - 30.7 (6.8)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.000228 (Kruskal-Wallis (anova)), Q value = 0.013

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.1 (13.2)
subtype1	13	58.2 (10.7)
subtype2	23	57.9 (11.6)
subtype3	6	50.5 (13.4)
subtype4	24	71.1 (10.7)

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.022 (Fisher's exact test), Q value = 0.95

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	4	3	2	1	21	10	6	6	5	1	1
subtype1	0	1	0	0	9	0	1	1	1	0	0
subtype2	0	0	2	1	9	4	4	2	1	0	0
subtype3	0	0	0	0	1	1	1	1	1	0	0
subtype4	4	2	0	0	2	5	0	2	2	1	1

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

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

P value = 0.0168 (Fisher's exact test), Q value = 0.76

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	9	16	36
subtype1	1	3	9
subtype2	0	8	15
subtype3	0	1	4
subtype4	8	4	8

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0681 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	35	18	7	1
subtype1	11	1	1	0
subtype2	15	7	1	0
subtype3	1	3	1	0
subtype4	8	7	4	1

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0875 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1A	MX
ALL	49	1	7
subtype1	12	0	0
subtype2	21	0	2
subtype3	5	0	0
subtype4	11	1	5

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0892 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	56
subtype1	2	11
subtype2	1	22
subtype3	0	6
subtype4	7	17

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.602 (Kruskal-Wallis (anova)), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	41	36.8 (20.8)
subtype1	6	49.1 (29.6)
subtype2	15	35.7 (16.4)
subtype3	5	37.2 (24.6)
subtype4	15	33.0 (20.0)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	WHITE
ALL	27	36
subtype1	8	5
subtype2	17	6
subtype3	2	4
subtype4	0	21

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	12	20	26	8

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.666 (logrank test), Q value = 1

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	64	20	0.0 - 33.3 (3.2)
subtype1	11	4	0.0 - 33.3 (0.3)
subtype2	19	4	0.1 - 20.1 (0.2)
subtype3	26	10	0.3 - 30.7 (6.8)
subtype4	8	2	0.1 - 10.4 (0.1)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0017 (Kruskal-Wallis (anova)), Q value = 0.082

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.1 (13.2)
subtype1	12	58.8 (8.9)
subtype2	20	56.6 (11.8)
subtype3	26	69.0 (13.6)
subtype4	8	58.1 (12.1)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0228 (Fisher's exact test), Q value = 0.96

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	4	3	2	1	21	10	6	6	5	1	1
subtype1	0	1	0	0	4	1	3	2	1	0	0
subtype2	0	0	2	0	9	4	2	2	1	0	0
subtype3	4	2	0	0	2	5	0	2	3	1	1
subtype4	0	0	0	1	6	0	1	0	0	0	0

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00688 (Fisher's exact test), Q value = 0.32

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	9	16	36
subtype1	1	1	10
subtype2	0	7	13
subtype3	8	4	9
subtype4	0	4	4

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.251 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	35	18	7	1
subtype1	6	5	1	0
subtype2	14	5	1	0
subtype3	8	7	5	1
subtype4	7	1	0	0

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.127 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1A	MX
ALL	49	1	7
subtype1	11	0	0
subtype2	18	0	2
subtype3	12	1	5
subtype4	8	0	0

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.138 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	56
subtype1	2	10
subtype2	1	19
subtype3	7	19
subtype4	0	8

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.675 (Kruskal-Wallis (anova)), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	41	36.8 (20.8)
subtype1	8	44.3 (24.3)
subtype2	12	36.7 (17.8)
subtype3	17	34.2 (22.1)
subtype4	4	33.8 (20.6)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	WHITE
ALL	27	36
subtype1	6	6
subtype2	14	6
subtype3	0	23
subtype4	7	1

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	60	54	10

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.413 (logrank test), Q value = 1

Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	37	0.0 - 122.1 (5.8)
subtype1	60	21	0.3 - 122.1 (7.9)
subtype2	47	13	0.0 - 53.9 (0.4)
subtype3	9	3	0.3 - 83.2 (6.2)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 9.65e-06 (Kruskal-Wallis (anova)), Q value = 0.00069

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	63.8 (12.7)
subtype1	60	68.0 (11.9)
subtype2	54	57.8 (10.9)
subtype3	10	70.2 (14.1)

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0031

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	7	4	5	1	33	22	16	8	6	4	2	1
subtype1	7	2	2	0	3	12	7	2	3	4	2	1
subtype2	0	1	2	1	27	9	8	3	3	0	0	0
subtype3	0	1	1	0	3	1	1	3	0	0	0	0

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00142 (Fisher's exact test), Q value = 0.071

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	21	30	62
subtype1	17	8	24
subtype2	3	20	31
subtype3	1	2	7

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.129 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	58	41	8	4
subtype1	19	21	5	4
subtype2	33	17	3	0
subtype3	6	3	0	0

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.0507 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	89	1	2	17
subtype1	32	1	2	11
subtype2	49	0	0	4
subtype3	8	0	0	2

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.587 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	106
subtype1	11	49
subtype2	6	48
subtype3	1	9

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.318 (Kruskal-Wallis (anova)), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	37.2 (21.3)
subtype1	34	39.2 (24.1)
subtype2	32	32.8 (15.4)
subtype3	7	48.1 (27.4)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	35	1	83
subtype1	0	0	55
subtype2	34	1	19
subtype3	1	0	9

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	44	20	35	25

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.237 (logrank test), Q value = 1

Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	37	0.0 - 122.1 (5.8)
subtype1	44	17	0.3 - 122.1 (7.3)
subtype2	20	6	1.0 - 83.2 (8.7)
subtype3	30	8	0.0 - 33.3 (0.3)
subtype4	22	6	0.1 - 53.9 (1.0)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000158 (Kruskal-Wallis (anova)), Q value = 0.0092

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	63.8 (12.7)
subtype1	44	66.3 (12.7)
subtype2	20	72.1 (10.5)
subtype3	35	58.4 (12.8)
subtype4	25	60.1 (9.9)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0015 (Fisher's exact test), Q value = 0.073

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	7	4	5	1	33	22	16	8	6	4	2	1
subtype1	6	2	2	0	1	9	5	1	2	3	1	1
subtype2	1	0	0	0	4	4	2	2	1	1	1	0
subtype3	0	1	2	1	12	7	7	3	2	0	0	0
subtype4	0	1	1	0	16	2	2	2	1	0	0	0

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00017 (Fisher's exact test), Q value = 0.0097

Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	21	30	62
subtype1	16	8	12
subtype2	1	2	14
subtype3	2	10	23
subtype4	2	10	13

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

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

Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	58	41	8	4
subtype1	13	17	3	3
subtype2	8	5	2	1
subtype3	19	13	2	0
subtype4	18	6	1	0

Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0805 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	89	1	2	17
subtype1	22	1	1	9
subtype2	13	0	1	3
subtype3	31	0	0	4
subtype4	23	0	0	1

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.55 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	106
subtype1	8	36
subtype2	4	16
subtype3	3	32
subtype4	3	22

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.698 (Kruskal-Wallis (anova)), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	37.2 (21.3)
subtype1	24	35.0 (18.5)
subtype2	12	47.4 (31.6)
subtype3	19	36.8 (20.1)
subtype4	18	33.9 (17.0)

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	35	1	83
subtype1	0	0	40
subtype2	0	0	19
subtype3	18	1	16
subtype4	17	0	8

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	56	50	18

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

P value = 0.455 (logrank test), Q value = 1

Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	37	0.0 - 122.1 (5.8)
subtype1	56	21	0.3 - 122.1 (7.9)
subtype2	43	12	0.0 - 53.9 (0.5)
subtype3	17	4	0.1 - 83.2 (3.8)

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 2.45e-05 (Kruskal-Wallis (anova)), Q value = 0.0015

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	63.8 (12.7)
subtype1	56	67.9 (12.2)
subtype2	50	57.6 (10.8)
subtype3	18	68.1 (12.6)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00066 (Fisher's exact test), Q value = 0.034

Table S64. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	7	4	5	1	33	22	16	8	6	4	2	1
subtype1	7	2	2	0	3	10	7	2	2	3	2	1
subtype2	0	1	2	1	25	8	7	3	3	0	0	0
subtype3	0	1	1	0	5	4	2	3	1	1	0	0

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00092 (Fisher's exact test), Q value = 0.047

Table S65. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	21	30	62
subtype1	17	8	20
subtype2	3	17	30
subtype3	1	5	12

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.135 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	58	41	8	4
subtype1	17	21	4	3
subtype2	31	15	3	0
subtype3	10	5	1	1

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0354 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	89	1	2	17
subtype1	28	1	2	11
subtype2	45	0	0	4
subtype3	16	0	0	2

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

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

Table S68. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	106
subtype1	11	45
subtype2	6	44
subtype3	1	17

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.132 (Kruskal-Wallis (anova)), Q value = 1

Table S69. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	37.2 (21.3)
subtype1	30	38.3 (22.5)
subtype2	30	31.6 (14.9)
subtype3	13	47.7 (27.4)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S70. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	35	1	83
subtype1	0	0	51
subtype2	31	1	18
subtype3	4	0	14

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	48	15	61

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

P value = 0.16 (logrank test), Q value = 1

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	37	0.0 - 122.1 (5.8)
subtype1	48	20	0.3 - 122.1 (7.3)
subtype2	15	3	1.0 - 83.2 (13.2)
subtype3	53	14	0.0 - 53.9 (0.5)

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.000356 (Kruskal-Wallis (anova)), Q value = 0.019

Table S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	63.8 (12.7)
subtype1	48	66.8 (12.5)
subtype2	15	71.1 (10.6)
subtype3	61	59.6 (12.0)

Figure S65. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 5e-05 (Fisher's exact test), Q value = 0.0031

Table S74. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	7	4	5	1	33	22	16	8	6	4	2	1
subtype1	6	2	2	0	1	9	5	1	2	3	2	1
subtype2	1	0	0	0	4	3	2	2	1	1	0	0
subtype3	0	2	3	1	28	10	9	5	3	0	0	0

Figure S66. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 6e-05 (Fisher's exact test), Q value = 0.0036

Table S75. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3+T4
ALL	21	30	62
subtype1	16	8	13
subtype2	1	1	13
subtype3	4	21	36

Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0261 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	58	41	8	4
subtype1	13	18	3	3
subtype2	8	3	2	1
subtype3	37	20	3	0

Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0131 (Fisher's exact test), Q value = 0.6

Table S77. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	89	1	2	17
subtype1	22	1	2	9
subtype2	12	0	0	3
subtype3	55	0	0	5

Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

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

Table S78. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	106
subtype1	9	39
subtype2	3	12
subtype3	6	55

Figure S70. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.251 (Kruskal-Wallis (anova)), Q value = 1

Table S79. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	37.2 (21.3)
subtype1	27	35.8 (20.0)
subtype2	8	53.2 (32.2)
subtype3	38	34.9 (18.4)

Figure S71. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00071

Table S80. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	35	1	83
subtype1	0	0	44
subtype2	0	0	14
subtype3	35	1	25

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

Methods & Data

Input

Cluster data file = ESCA-TP.mergedcluster.txt
Clinical data file = ESCA-TP.merged_data.txt
Number of patients = 127
Number of clustering approaches = 8
Number of selected clinical features = 9
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.

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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