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 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

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)
[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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