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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle