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 5 different clustering approaches and 8 clinical features across 290 patients, 5 significant findings detected with P value < 0.05.

3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', '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 correlate to 'PATHOLOGY.N'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGY.N'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 5 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	0.208	0.201	0.0699	0.344	0.222
AGE	ANOVA	0.0437	0.579	0.355	0.613	0.695
GENDER	Fisher's exact test	0.0065	0.711	0.133	0.325	0.746
PATHOLOGY T	Chi-square test	0.0162	0.64	0.141	0.168	0.142
PATHOLOGY N	Chi-square test	0.331	0.338	0.0281	0.0273	0.541
PATHOLOGICSPREAD(M)	Fisher's exact test	0.777	1	0.747	0.634	0.379
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.944	0.719	0.895	0.698	0.798
NEOADJUVANT THERAPY	Fisher's exact test	0.721	0.624	0.408	0.583	0.494

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	90	97	103

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.208 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	288	120	0.1 - 210.9 (13.8)
subtype1	89	39	1.0 - 114.9 (13.6)
subtype2	97	37	0.1 - 142.5 (16.6)
subtype3	102	44	0.1 - 210.9 (13.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0437 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	290	61.4 (12.2)
subtype1	90	58.8 (12.7)
subtype2	97	61.9 (10.9)
subtype3	103	63.1 (12.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0065 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	208	82
subtype1	70	20
subtype2	76	21
subtype3	62	41

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0162 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	19	81	77	103
subtype1	4	20	28	38
subtype2	2	32	28	29
subtype3	13	29	21	36

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.331 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	113	44	108	4
subtype1	41	17	27	1
subtype2	33	10	43	1
subtype3	39	17	38	2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.777 (Fisher's exact test)

Table S7. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	286	3
subtype1	89	0
subtype2	96	1
subtype3	101	2

Figure S6. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.944 (Fisher's exact test)

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

nPatients	NO	YES
ALL	212	78
subtype1	67	23
subtype2	70	27
subtype3	75	28

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 = 0.721 (Fisher's exact test)

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

nPatients	NO	YES
ALL	48	242
subtype1	15	75
subtype2	18	79
subtype3	15	88

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	65	90

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.201 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	227	95	0.1 - 210.9 (14.0)
subtype1	72	33	0.1 - 210.9 (15.5)
subtype2	65	30	1.5 - 142.5 (13.0)
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 = 0.579 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	228	61.2 (12.3)
subtype1	73	60.3 (13.2)
subtype2	65	60.8 (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 = 0.711 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	169	59
subtype1	54	19
subtype2	46	19
subtype3	69	21

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 = 0.64 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	11	61	64	83
subtype1	3	15	22	30
subtype2	5	20	16	24
subtype3	3	26	26	29

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 = 0.338 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	85	37	88	3
subtype1	30	17	22	1
subtype2	21	10	31	1
subtype3	34	10	35	1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S16. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	225	2
subtype1	72	1
subtype2	64	0
subtype3	89	1

Figure S14. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.719 (Fisher's exact test)

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

nPatients	NO	YES
ALL	166	62
subtype1	55	18
subtype2	45	20
subtype3	66	24

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	190
subtype1	10	63
subtype2	13	52
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	64	70	94

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0699 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	227	95	0.1 - 210.9 (14.0)
subtype1	64	23	0.1 - 135.3 (11.6)
subtype2	70	34	1.5 - 142.5 (13.2)
subtype3	93	38	0.1 - 210.9 (15.8)

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 = 0.355 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	228	61.2 (12.3)
subtype1	64	63.1 (11.2)
subtype2	70	60.5 (13.6)
subtype3	94	60.4 (12.0)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.133 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	169	59
subtype1	48	16
subtype2	46	24
subtype3	75	19

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 = 0.141 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	11	61	64	83
subtype1	2	23	16	18
subtype2	6	20	19	25
subtype3	3	18	29	40

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 = 0.0281 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	85	37	88	3
subtype1	19	5	31	1
subtype2	22	12	32	1
subtype3	44	20	25	1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.747 (Fisher's exact test)

Table S25. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	225	2
subtype1	63	1
subtype2	69	0
subtype3	93	1

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.895 (Fisher's exact test)

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

nPatients	NO	YES
ALL	166	62
subtype1	46	18
subtype2	50	20
subtype3	70	24

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 = 0.408 (Fisher's exact test)

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

nPatients	NO	YES
ALL	38	190
subtype1	12	52
subtype2	14	56
subtype3	12	82

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	75	102	81

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.344 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	256	104	0.1 - 142.5 (14.0)
subtype1	74	29	0.1 - 126.1 (11.7)
subtype2	102	39	1.0 - 142.5 (14.6)
subtype3	80	36	1.8 - 108.3 (17.1)

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 = 0.613 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	258	61.0 (12.2)
subtype1	75	59.9 (12.9)
subtype2	102	61.7 (11.2)
subtype3	81	61.2 (12.9)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.325 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	188	70
subtype1	59	16
subtype2	74	28
subtype3	55	26

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 = 0.168 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	17	69	75	90
subtype1	3	21	18	31
subtype2	4	25	33	36
subtype3	10	23	24	23

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 = 0.0273 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	100	39	100	4
subtype1	31	14	22	3
subtype2	45	9	41	1
subtype3	24	16	37	0

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

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.634 (Fisher's exact test)

Table S34. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	254	3
subtype1	74	1
subtype2	100	2
subtype3	80	0

Figure S30. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.698 (Fisher's exact test)

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

nPatients	NO	YES
ALL	183	75
subtype1	53	22
subtype2	75	27
subtype3	55	26

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 = 0.583 (Fisher's exact test)

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

nPatients	NO	YES
ALL	47	211
subtype1	11	64
subtype2	19	83
subtype3	17	64

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
Number of samples	13	145	100

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.222 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	256	104	0.1 - 142.5 (14.0)
subtype1	13	5	3.7 - 33.1 (13.0)
subtype2	144	54	0.1 - 142.5 (13.4)
subtype3	99	45	1.5 - 129.2 (15.7)

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 = 0.695 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	258	61.0 (12.2)
subtype1	13	62.2 (12.8)
subtype2	145	61.5 (11.4)
subtype3	100	60.2 (13.3)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.746 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	188	70
subtype1	10	3
subtype2	108	37
subtype3	70	30

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 = 0.142 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	17	69	75	90
subtype1	1	2	6	3
subtype2	5	37	40	57
subtype3	11	30	29	30

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 = 0.541 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	100	39	100	4
subtype1	5	2	4	0
subtype2	62	20	49	3
subtype3	33	17	47	1

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.379 (Fisher's exact test)

Table S43. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	254	3
subtype1	13	0
subtype2	142	3
subtype3	99	0

Figure S38. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.798 (Fisher's exact test)

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

nPatients	NO	YES
ALL	183	75
subtype1	9	4
subtype2	105	40
subtype3	69	31

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 = 0.494 (Fisher's exact test)

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

nPatients	NO	YES
ALL	47	211
subtype1	3	10
subtype2	23	122
subtype3	21	79

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 = 290
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

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)

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