Acute Myeloid Leukemia: 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 3 clinical features across 197 patients, 5 significant findings detected with P value < 0.05.

- 7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.

- 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 2 subtypes that correlate to 'AGE'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 3 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 5 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER
Statistical Tests | logrank test | ANOVA | Fisher's exact test
METHLYATION CNMF | 2.84e-07 | 7.31e-08 | 0.37
RNAseq CNMF subtypes | 0.125 | 0.39 | 0.312
RNAseq cHierClus subtypes | 0.683 | 0.0292 | 1
MIRseq CNMF subtypes | 0.00738 | 0.221 | 0.797
MIRseq cHierClus subtypes | 0.00985 | 0.077 | 0.767

Clustering Approach #1: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7
--- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 40 | 19 | 21 | 32 | 49 | 18 | 13

'METHLYATION CNMF' versus 'Time to Death'

P value = 2.84e-07 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 105 | 0.9 - 94.1 (12.0)
subtype1 | 35 | 25 | 1.0 - 62.0 (10.0)
subtype2 | 17 | 5 | 0.9 - 84.0 (26.0)
subtype3 | 20 | 14 | 1.0 - 56.1 (15.0)
subtype4 | 28 | 10 | 1.0 - 94.1 (20.5)
subtype5 | 41 | 31 | 0.9 - 52.0 (10.1)
subtype6 | 15 | 11 | 2.0 - 69.0 (8.0)
subtype7 | 12 | 9 | 4.0 - 73.0 (13.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 7.31e-08 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 192 | 55.1 (16.1)
subtype1 | 40 | 55.6 (15.4)
subtype2 | 19 | 48.9 (15.8)
subtype3 | 21 | 60.4 (13.5)
subtype4 | 32 | 43.9 (15.7)
subtype5 | 49 | 63.7 (11.9)
subtype6 | 18 | 57.9 (14.2)
subtype7 | 13 | 45.5 (17.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.37 (Chi-square test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 89 | 103
subtype1 | 22 | 18
subtype2 | 11 | 8
subtype3 | 8 | 13
subtype4 | 16 | 16
subtype5 | 20 | 29
subtype6 | 9 | 9
subtype7 | 3 | 10

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S5. Get Full Table Description of clustering approach #2: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 74 | 57 | 48

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.125 (logrank test)

Table S6. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 157 | 97 | 0.9 - 94.1 (12.0)
subtype1 | 67 | 39 | 1.0 - 94.1 (16.1)
subtype2 | 50 | 32 | 0.9 - 75.1 (11.0)
subtype3 | 40 | 26 | 0.9 - 62.0 (9.5)

Figure S4. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.39 (ANOVA)

Table S7. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 55.0 (15.9)
subtype1 | 74 | 53.8 (17.1)
subtype2 | 57 | 57.4 (13.6)
subtype3 | 48 | 54.0 (16.7)

Figure S5. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.312 (Fisher's exact test)

Table S8. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 84 | 95
subtype1 | 33 | 41
subtype2 | 24 | 33
subtype3 | 27 | 21

Figure S6. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S9. Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 61 | 118

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.683 (logrank test)

Table S10. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 157 | 97 | 0.9 - 94.1 (12.0)
subtype1 | 52 | 33 | 0.9 - 75.1 (11.5)
subtype2 | 105 | 64 | 0.9 - 94.1 (12.9)

Figure S7. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0292 (t-test)

Table S11. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 179 | 55.0 (15.9)
subtype1 | 61 | 58.3 (12.8)
subtype2 | 118 | 53.3 (17.1)

Figure S8. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S12. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 84 | 95
subtype1 | 29 | 32
subtype2 | 55 | 63

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 86 | 40 | 61

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00738 (logrank test)

Table S14. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 163 | 101 | 0.9 - 94.1 (12.0)
subtype1 | 75 | 54 | 0.9 - 73.0 (10.0)
subtype2 | 36 | 18 | 0.9 - 62.0 (14.5)
subtype3 | 52 | 29 | 1.0 - 94.1 (15.0)

Figure S10. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.221 (ANOVA)

Table S15. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 187 | 55.1 (16.0)
subtype1 | 86 | 56.1 (14.5)
subtype2 | 40 | 57.2 (14.5)
subtype3 | 61 | 52.2 (18.7)

Figure S11. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.797 (Fisher's exact test)

Table S16. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 101
subtype1 | 39 | 47
subtype2 | 17 | 23
subtype3 | 30 | 31

Figure S12. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S17. Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 38 | 82 | 67

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00985 (logrank test)

Table S18. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 163 | 101 | 0.9 - 94.1 (12.0)
subtype1 | 34 | 16 | 0.9 - 62.0 (14.5)
subtype2 | 71 | 50 | 0.9 - 69.0 (10.0)
subtype3 | 58 | 35 | 1.0 - 94.1 (15.0)

Figure S13. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.077 (ANOVA)

Table S19. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 187 | 55.1 (16.0)
subtype1 | 38 | 58.2 (14.2)
subtype2 | 82 | 56.4 (13.6)
subtype3 | 67 | 51.6 (19.1)

Figure S14. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.767 (Fisher's exact test)

Table S20. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 101
subtype1 | 16 | 22
subtype2 | 40 | 42
subtype3 | 30 | 37

Figure S15. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data
Input

- Cluster data file = LAML.mergedcluster.txt

- Clinical data file = LAML.clin.merged.picked.txt

- Number of patients = 197

- Number of clustering approaches = 5

- Number of selected clinical features = 3

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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