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 5 different clustering approaches and 8 clinical features across 311 patients, 3 significant findings detected with P value < 0.05.

3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER' and 'PATHOLOGY.T'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'NEOADJUVANT.THERAPY'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 3 significant findings detected.


Clinical Features	Statistical Tests	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
Time to Death	logrank test	0.235	0.199	0.0722	0.665	0.145
AGE	ANOVA	0.071	0.559	0.289	0.723	0.688
GENDER	Fisher's exact test	0.00615	0.739	0.187	0.0643	0.637
PATHOLOGY T	Chi-square test	0.0407	0.676	0.43	0.121	0.395
PATHOLOGY N	Chi-square test	0.549	0.614	0.0869	0.109	0.191
TUMOR STAGE	Chi-square test	0.569	0.644	0.638	0.451	0.321
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.65	0.76	0.719	0.533	0.551
NEOADJUVANT THERAPY	Fisher's exact test	0.922	0.624	0.176	0.689	0.0179

Clustering Approach #1: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	94	101	97

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.235 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	290	121	0.1 - 210.9 (14.2)
subtype1	93	41	0.1 - 126.1 (13.0)
subtype2	101	39	0.1 - 142.5 (16.6)
subtype3	96	41	0.2 - 210.9 (13.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.071 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	292	61.3 (12.2)
subtype1	94	59.0 (13.2)
subtype2	101	61.9 (10.8)
subtype3	97	62.9 (12.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00615 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	82	210
subtype1	20	74
subtype2	23	78
subtype3	39	58

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0407 (Chi-square test)

Table S5. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	21	76	61	99
subtype1	3	22	25	38
subtype2	5	26	22	26
subtype3	13	28	14	35

Figure S4. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.549 (Chi-square test)

Table S6. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	96	31	97	4
subtype1	38	9	31	0
subtype2	28	8	31	2
subtype3	30	14	35	2

Figure S5. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.569 (Chi-square test)

Table S7. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	15	46	40	151
subtype1	3	16	15	54
subtype2	3	15	13	45
subtype3	9	15	12	52

Figure S6. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.65 (Fisher's exact test)

Table S8. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	78	214
subtype1	22	72
subtype2	28	73
subtype3	28	69

Figure S7. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.922 (Fisher's exact test)

Table S9. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	48	244
subtype1	15	79
subtype2	18	83
subtype3	15	82

Figure S8. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S10. Get Full Table Description of clustering approach #2: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	73	66	90

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.199 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	228	96	0.1 - 210.9 (14.2)
subtype1	72	33	0.1 - 210.9 (15.5)
subtype2	66	31	1.5 - 142.5 (13.1)
subtype3	90	32	0.1 - 135.3 (13.4)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.559 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	229	61.1 (12.3)
subtype1	73	60.3 (13.2)
subtype2	66	60.6 (12.5)
subtype3	90	62.2 (11.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.739 (Fisher's exact test)

Table S13. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	59	170
subtype1	19	54
subtype2	19	47
subtype3	21	69

Figure S11. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.676 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	12	55	47	82
subtype1	2	18	14	32
subtype2	6	17	15	22
subtype3	4	20	18	28

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.614 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	73	23	76	3
subtype1	27	9	23	0
subtype2	20	6	29	1
subtype3	26	8	24	2

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.644 (Chi-square test)

Table S16. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	9	34	29	121
subtype1	2	11	11	42
subtype2	5	10	6	38
subtype3	2	13	12	41

Figure S14. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.76 (Fisher's exact test)

Table S17. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	62	167
subtype1	18	55
subtype2	20	46
subtype3	24	66

Figure S15. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.624 (Fisher's exact test)

Table S18. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	38	191
subtype1	10	63
subtype2	13	53
subtype3	15	75

Figure S16. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S19. Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	72	91	66

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0722 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	228	96	0.1 - 210.9 (14.2)
subtype1	72	35	1.5 - 142.5 (13.2)
subtype2	90	37	0.1 - 210.9 (15.5)
subtype3	66	24	0.1 - 135.3 (12.6)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.289 (ANOVA)

Table S21. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	229	61.1 (12.3)
subtype1	72	60.2 (13.6)
subtype2	91	60.5 (12.0)
subtype3	66	63.2 (11.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.187 (Fisher's exact test)

Table S22. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	59	170
subtype1	24	48
subtype2	19	72
subtype3	16	50

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.43 (Chi-square test)

Table S23. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	12	55	47	82
subtype1	7	18	17	26
subtype2	3	20	18	39
subtype3	2	17	12	17

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0869 (Chi-square test)

Table S24. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	73	23	76	3
subtype1	20	8	33	1
subtype2	38	11	24	0
subtype3	15	4	19	2

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.638 (Chi-square test)

Table S25. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	9	34	29	121
subtype1	5	11	7	44
subtype2	3	13	15	49
subtype3	1	10	7	28

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.719 (Fisher's exact test)

Table S26. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	62	167
subtype1	21	51
subtype2	22	69
subtype3	19	47

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.176 (Fisher's exact test)

Table S27. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	38	191
subtype1	15	57
subtype2	10	81
subtype3	13	53

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S28. Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	83	131	94

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.665 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	306	123	0.1 - 210.9 (14.9)
subtype1	82	30	0.1 - 210.9 (12.1)
subtype2	131	52	0.1 - 142.5 (14.3)
subtype3	93	41	1.8 - 126.1 (17.8)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.723 (ANOVA)

Table S30. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	308	61.1 (12.1)
subtype1	83	60.4 (12.5)
subtype2	131	61.7 (11.4)
subtype3	94	60.9 (12.7)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0643 (Fisher's exact test)

Table S31. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	86	222
subtype1	16	67
subtype2	37	94
subtype3	33	61

Figure S27. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.121 (Chi-square test)

Table S32. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	24	78	62	103
subtype1	5	23	11	30
subtype2	7	26	29	47
subtype3	12	29	22	26

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.109 (Chi-square test)

Table S33. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	99	32	101	5
subtype1	30	8	21	2
subtype2	44	9	41	3
subtype3	25	15	39	0

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.451 (Chi-square test)

Table S34. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	17	46	41	158
subtype1	4	15	8	41
subtype2	4	16	18	68
subtype3	9	15	15	49

Figure S30. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.533 (Fisher's exact test)

Table S35. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	231
subtype1	21	62
subtype2	29	102
subtype3	27	67

Figure S31. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.689 (Fisher's exact test)

Table S36. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	48	260
subtype1	11	72
subtype2	20	111
subtype3	17	77

Figure S32. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	13	12	167	116

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.145 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	306	123	0.1 - 210.9 (14.9)
subtype1	13	5	0.2 - 56.7 (12.0)
subtype2	12	5	9.0 - 84.4 (12.8)
subtype3	166	61	0.1 - 210.9 (14.0)
subtype4	115	52	0.5 - 156.5 (17.1)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.688 (ANOVA)

Table S39. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	308	61.1 (12.1)
subtype1	13	58.4 (13.2)
subtype2	12	61.0 (11.8)
subtype3	167	61.8 (11.5)
subtype4	116	60.4 (12.9)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test)

Table S40. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	86	222
subtype1	4	9
subtype2	4	8
subtype3	42	125
subtype4	36	80

Figure S35. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.395 (Chi-square test)

Table S41. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	24	78	62	103
subtype1	2	5	2	3
subtype2	0	3	2	5
subtype3	8	38	30	60
subtype4	14	32	28	35

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.191 (Chi-square test)

Table S42. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	99	32	101	5
subtype1	8	1	3	0
subtype2	5	0	5	0
subtype3	54	17	42	4
subtype4	32	14	51	1

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

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.321 (Chi-square test)

Table S43. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	17	46	41	158
subtype1	2	4	2	4
subtype2	0	1	1	8
subtype3	5	24	23	80
subtype4	10	17	15	66

Figure S38. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.551 (Fisher's exact test)

Table S44. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	231
subtype1	3	10
subtype2	4	8
subtype3	37	130
subtype4	33	83

Figure S39. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0179 (Fisher's exact test)

Table S45. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	48	260
subtype1	0	13
subtype2	3	9
subtype3	19	148
subtype4	26	90

Figure S40. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = HNSC.mergedcluster.txt
Clinical data file = HNSC.clin.merged.picked.txt
Number of patients = 311
Number of clustering approaches = 5
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

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)

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