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.

- 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)
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