Acute Myeloid Leukemia: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary blood tumor (peripheral) cohort)

Acute Myeloid Leukemia: Correlation between molecular cancer subtypes and selected clinical features
(primary blood tumor (peripheral) cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

6 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 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 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 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER
Statistical Tests	logrank test	ANOVA	Fisher's exact test
METHLYATION CNMF	2.96e-05 (0.000415)	9.48e-09 (1.42e-07)	0.77 (1.00)
RNAseq CNMF subtypes	0.308 (1.00)	0.39 (1.00)	0.312 (1.00)
RNAseq cHierClus subtypes	0.683 (1.00)	0.0292 (0.359)	1 (1.00)
MIRseq CNMF subtypes	0.0276 (0.359)	0.221 (1.00)	0.797 (1.00)
MIRseq cHierClus subtypes	0.0358 (0.393)	0.077 (0.77)	0.767 (1.00)

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
Number of samples	48	18	26	40	48	14

'METHLYATION CNMF' versus 'Time to Death'

P value = 2.96e-05 (logrank test), Q value = 0.00041

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	169	106	0.9 - 94.1 (12.0)
subtype1	42	30	1.0 - 69.0 (8.0)
subtype2	16	4	0.9 - 84.0 (36.0)
subtype3	24	17	1.0 - 56.1 (15.5)
subtype4	36	15	1.0 - 94.1 (19.5)
subtype5	40	31	0.9 - 52.0 (10.5)
subtype6	11	9	1.0 - 42.0 (10.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 9.48e-09 (ANOVA), Q value = 1.4e-07

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

	nPatients	Mean (Std.Dev)
ALL	194	55.1 (16.0)
subtype1	48	56.6 (14.2)
subtype2	18	47.4 (14.8)
subtype3	26	61.3 (13.3)
subtype4	40	45.1 (16.9)
subtype5	48	63.8 (12.0)
subtype6	14	47.4 (15.7)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.77 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	89	105
subtype1	24	24
subtype2	10	8
subtype3	13	13
subtype4	18	22
subtype5	19	29
subtype6	5	9

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 = 0.308 (logrank test), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 0.36

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), Q value = 1

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 = 0.0276 (logrank test), Q value = 0.36

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), Q value = 1

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), Q value = 1

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 = 0.0358 (logrank test), Q value = 0.39

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), Q value = 0.77

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), Q value = 1

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-TB.mergedcluster.txt
Clinical data file = LAML-TP.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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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)

[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)

[8] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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