Head and Neck Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

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 8 clinical features across 311 patients, 9 significant findings detected with P value < 0.05.

5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER' and 'PATHOLOGY.T'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGY.N', and 'NEOADJUVANT.THERAPY'.
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 3 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 'NEOADJUVANT.THERAPY'.
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 8 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 9 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	PATHOLOGY T	PATHOLOGY N	TUMOR STAGE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Chi-square test	Chi-square test	Chi-square test	Fisher's exact test	Fisher's exact test
CN CNMF	0.00367	0.0214	0.178	0.686	0.153	0.253	0.436	0.288
METHLYATION CNMF	0.26	0.071	0.00615	0.0407	0.549	0.569	0.65	0.922
RPPA CNMF subtypes	0.0351	0.434	0.139	0.216	0.0063	0.0532	0.301	0.023
RPPA cHierClus subtypes	0.103	0.303	0.842	0.194	0.0534	0.103	0.595	0.0821
RNAseq CNMF subtypes	0.15	0.846	0.307	0.789	0.182	0.457	0.71	0.218
RNAseq cHierClus subtypes	0.119	0.33	0.372	0.172	0.0884	0.361	0.441	0.0304
MIRseq CNMF subtypes	0.72	0.723	0.0643	0.121	0.109	0.451	0.533	0.689
MIRseq cHierClus subtypes	0.161	0.688	0.637	0.395	0.191	0.321	0.551	0.0179

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels	1	2	3	4	5
Number of samples	66	75	36	101	10

'CN CNMF' versus 'Time to Death'

P value = 0.00367 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	285	119	0.1 - 210.9 (14.3)
subtype1	66	35	0.2 - 142.5 (12.1)
subtype2	74	32	0.1 - 129.2 (18.0)
subtype3	36	15	0.1 - 111.1 (11.6)
subtype4	99	33	0.8 - 210.9 (17.1)
subtype5	10	4	3.1 - 89.8 (11.2)

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

'CN CNMF' versus 'AGE'

P value = 0.0214 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	288	61.2 (12.2)
subtype1	66	61.8 (11.8)
subtype2	75	57.7 (13.2)
subtype3	36	60.8 (10.0)
subtype4	101	63.8 (12.3)
subtype5	10	59.5 (7.4)

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

'CN CNMF' versus 'GENDER'

P value = 0.178 (Chi-square test)

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

nPatients	FEMALE	MALE
ALL	81	207
subtype1	15	51
subtype2	16	59
subtype3	10	26
subtype4	37	64
subtype5	3	7

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.686 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	21	74	60	98
subtype1	5	13	12	25
subtype2	3	21	21	24
subtype3	2	7	7	13
subtype4	11	30	17	32
subtype5	0	3	3	4

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

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.153 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	95	31	94	4
subtype1	13	5	31	2
subtype2	27	10	24	1
subtype3	11	3	11	1
subtype4	41	11	25	0
subtype5	3	2	3	0

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

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.253 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	15	45	40	148
subtype1	4	5	5	40
subtype2	3	12	16	38
subtype3	2	4	3	20
subtype4	6	21	15	44
subtype5	0	3	1	6

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

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.436 (Chi-square test)

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

nPatients	NO	YES
ALL	76	212
subtype1	23	43
subtype2	20	55
subtype3	9	27
subtype4	22	79
subtype5	2	8

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

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.288 (Chi-square test)

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

nPatients	NO	YES
ALL	47	241
subtype1	16	50
subtype2	13	62
subtype3	5	31
subtype4	12	89
subtype5	1	9

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	94	101	97

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.26 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	289	120	0.1 - 210.9 (14.3)
subtype1	93	41	0.1 - 126.1 (13.0)
subtype2	101	39	0.1 - 142.5 (16.6)
subtype3	95	40	0.2 - 210.9 (13.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.071 (ANOVA)

Table S12. Clustering Approach #2: '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 S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00615 (Fisher's exact test)

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0407 (Chi-square test)

Table S14. Clustering Approach #2: '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 S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.549 (Chi-square test)

Table S15. Clustering Approach #2: '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 S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.569 (Chi-square test)

Table S16. Clustering Approach #2: '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 S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

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

P value = 0.65 (Fisher's exact test)

Table S17. Clustering Approach #2: '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 S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.922 (Fisher's exact test)

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

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

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	82	85	45

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0351 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	107	0.1 - 210.9 (13.2)
subtype1	82	42	1.5 - 129.2 (12.1)
subtype2	85	47	0.1 - 210.9 (13.1)
subtype3	45	18	2.1 - 156.5 (20.5)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	82	61.1 (12.1)
subtype2	85	63.4 (12.1)
subtype3	45	61.4 (12.7)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.139 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	62	150
subtype1	19	63
subtype2	25	60
subtype3	18	27

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	13	59	53	79
subtype1	3	24	16	34
subtype2	6	18	27	32
subtype3	4	17	10	13

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0063 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	73	20	79	4
subtype1	27	9	30	1
subtype2	19	8	40	2
subtype3	27	3	9	1

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

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0532 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	9	39	31	121
subtype1	1	17	12	47
subtype2	4	9	12	55
subtype3	4	13	7	19

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

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

P value = 0.301 (Fisher's exact test)

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

nPatients	NO	YES
ALL	56	156
subtype1	25	57
subtype2	23	62
subtype3	8	37

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

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.023 (Fisher's exact test)

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

nPatients	NO	YES
ALL	36	176
subtype1	18	64
subtype2	16	69
subtype3	2	43

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	115	81	16

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.103 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	107	0.1 - 210.9 (13.2)
subtype1	115	56	0.1 - 210.9 (13.2)
subtype2	81	42	1.5 - 156.5 (13.6)
subtype3	16	9	3.3 - 52.3 (11.6)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.303 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	115	63.3 (11.2)
subtype2	81	61.1 (13.1)
subtype3	16	59.4 (14.3)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.842 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	62	150
subtype1	35	80
subtype2	22	59
subtype3	5	11

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.194 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	13	59	53	79
subtype1	7	25	36	42
subtype2	5	30	13	30
subtype3	1	4	4	7

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0534 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	73	20	79	4
subtype1	39	8	46	3
subtype2	32	11	22	1
subtype3	2	1	11	0

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

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.103 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	9	39	31	121
subtype1	6	16	18	69
subtype2	3	21	13	39
subtype3	0	2	0	13

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

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

P value = 0.595 (Fisher's exact test)

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

nPatients	NO	YES
ALL	56	156
subtype1	30	85
subtype2	20	61
subtype3	6	10

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

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0821 (Fisher's exact test)

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

nPatients	NO	YES
ALL	36	176
subtype1	19	96
subtype2	11	70
subtype3	6	10

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	107	88	91

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.15 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	283	119	0.1 - 210.9 (14.8)
subtype1	107	41	0.1 - 135.3 (13.3)
subtype2	86	40	0.2 - 142.5 (15.7)
subtype3	90	38	0.1 - 210.9 (15.3)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.846 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	286	61.3 (12.2)
subtype1	107	61.8 (11.1)
subtype2	88	61.1 (12.7)
subtype3	91	60.9 (13.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.307 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	78	208
subtype1	24	83
subtype2	25	63
subtype3	29	62

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.789 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	20	76	58	98
subtype1	5	24	23	33
subtype2	9	25	18	29
subtype3	6	27	17	36

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.182 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	95	31	94	4
subtype1	33	9	29	2
subtype2	24	10	41	1
subtype3	38	12	24	1

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.457 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	14	46	39	148
subtype1	2	15	16	49
subtype2	7	12	10	51
subtype3	5	19	13	48

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

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

P value = 0.71 (Fisher's exact test)

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

nPatients	NO	YES
ALL	75	211
subtype1	26	81
subtype2	26	62
subtype3	23	68

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.218 (Fisher's exact test)

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

nPatients	NO	YES
ALL	46	240
subtype1	18	89
subtype2	18	70
subtype3	10	81

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	86	101	99

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.119 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	283	119	0.1 - 210.9 (14.8)
subtype1	86	30	0.1 - 135.3 (14.6)
subtype2	100	44	0.1 - 210.9 (14.4)
subtype3	97	45	1.5 - 142.5 (15.1)

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 = 0.33 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	286	61.3 (12.2)
subtype1	86	62.6 (10.8)
subtype2	101	61.5 (12.4)
subtype3	99	59.9 (13.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.372 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	78	208
subtype1	19	67
subtype2	28	73
subtype3	31	68

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.172 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	20	76	58	98
subtype1	4	20	16	26
subtype2	3	29	19	41
subtype3	13	27	23	31

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0884 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	95	31	94	4
subtype1	22	7	25	2
subtype2	44	12	24	1
subtype3	29	12	45	1

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.361 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	14	46	39	148
subtype1	2	12	9	40
subtype2	3	20	17	51
subtype3	9	14	13	57

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

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

P value = 0.441 (Fisher's exact test)

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

nPatients	NO	YES
ALL	75	211
subtype1	24	62
subtype2	22	79
subtype3	29	70

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0304 (Fisher's exact test)

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

nPatients	NO	YES
ALL	46	240
subtype1	15	71
subtype2	9	92
subtype3	22	77

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

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S55. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	83	131	94

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.72 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	305	122	0.1 - 210.9 (15.0)
subtype1	82	30	0.1 - 210.9 (12.1)
subtype2	131	52	0.1 - 142.5 (14.3)
subtype3	92	40	1.8 - 126.1 (18.1)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.723 (ANOVA)

Table S57. Clustering Approach #7: '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 S50. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0643 (Fisher's exact test)

Table S58. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.121 (Chi-square test)

Table S59. Clustering Approach #7: '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 S52. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.109 (Chi-square test)

Table S60. Clustering Approach #7: '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 S53. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.451 (Chi-square test)

Table S61. Clustering Approach #7: '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 S54. Get High-res Image Clustering Approach #7: '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 S62. Clustering Approach #7: '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 S55. Get High-res Image Clustering Approach #7: '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 S63. Clustering Approach #7: '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 S56. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #8: '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 = 0.161 (logrank test)

Table S65. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	305	122	0.1 - 210.9 (15.0)
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	114	51	0.5 - 156.5 (17.1)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.688 (ANOVA)

Table S66. Clustering Approach #8: '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 S58. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test)

Table S67. Clustering Approach #8: '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 S59. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.395 (Chi-square test)

Table S68. Clustering Approach #8: '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 S60. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.191 (Chi-square test)

Table S69. Clustering Approach #8: '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 S61. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.321 (Chi-square test)

Table S70. Clustering Approach #8: '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 S62. Get High-res Image Clustering Approach #8: '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 S71. Clustering Approach #8: '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 S63. Get High-res Image Clustering Approach #8: '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 S72. Clustering Approach #8: '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 S64. Get High-res Image Clustering Approach #8: '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 = 8
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

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

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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

[6] 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)

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