Correlation between aggregated molecular cancer subtypes and selected clinical features

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15D8QT7

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 10 different clustering approaches and 6 clinical features across 47 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

2 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 2 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.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
8 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'ETHNICITY'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 6 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	HISTOLOGICAL TYPE	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.435 (0.842)	0.554 (0.842)	0.245 (0.708)	0.201 (0.708)	0.422 (0.842)	1 (1.00)
METHLYATION CNMF	0.307 (0.708)	0.307 (0.708)	0.561 (0.842)	0.193 (0.708)	0.0996 (0.705)	0.74 (1.00)
RPPA CNMF subtypes	0.0626 (0.626)	0.803 (1.00)	0.901 (1.00)	0.483 (0.842)	0.298 (0.708)	0.868 (1.00)
RPPA cHierClus subtypes	0.252 (0.708)	0.616 (0.901)	1 (1.00)	0.478 (0.842)	0.241 (0.708)	1 (1.00)
RNAseq CNMF subtypes	0.152 (0.705)	0.153 (0.705)	0.151 (0.705)	0.483 (0.842)	0.124 (0.705)	1 (1.00)
RNAseq cHierClus subtypes	0.0758 (0.649)	0.547 (0.842)	0.293 (0.708)	0.635 (0.907)	0.524 (0.842)	1 (1.00)
MIRSEQ CNMF	0.854 (1.00)	0.947 (1.00)	0.192 (0.708)	0.0594 (0.626)	0.866 (1.00)	0.271 (0.708)
MIRSEQ CHIERARCHICAL	0.548 (0.842)	0.882 (1.00)	0.185 (0.708)	0.0277 (0.549)	0.976 (1.00)	0.0164 (0.491)
MIRseq Mature CNMF subtypes	0.818 (1.00)	0.511 (0.842)	0.355 (0.761)	0.0366 (0.549)	0.33 (0.733)	0.724 (1.00)
MIRseq Mature cHierClus subtypes	0.918 (1.00)	0.754 (1.00)	0.47 (0.842)	0.24 (0.708)	0.119 (0.705)	0.00209 (0.125)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2
Number of samples	29	18

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.435 (logrank test), Q value = 0.84

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	9	0.0 - 211.2 (27.3)
subtype1	28	7	0.0 - 211.2 (26.0)
subtype2	18	2	0.6 - 196.6 (29.2)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.554 (Wilcoxon-test), Q value = 0.84

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	47	56.3 (14.1)
subtype1	29	55.0 (14.9)
subtype2	18	58.3 (12.9)

Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.245 (Fisher's exact test), Q value = 0.71

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	26	21
subtype1	14	15
subtype2	12	6

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.201 (Fisher's exact test), Q value = 0.71

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	40	3	4
subtype1	24	1	4
subtype2	16	2	0

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.422 (Fisher's exact test), Q value = 0.84

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	18	1	28
subtype1	9	1	19
subtype2	9	0	9

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	35
subtype1	7	22
subtype2	5	13

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S8. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2
Number of samples	24	23

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.307 (logrank test), Q value = 0.71

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	9	0.0 - 211.2 (27.3)
subtype1	23	3	1.9 - 196.6 (37.2)
subtype2	23	6	0.0 - 211.2 (25.9)

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.307 (Wilcoxon-test), Q value = 0.71

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	47	56.3 (14.1)
subtype1	24	54.2 (15.0)
subtype2	23	58.5 (13.1)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.561 (Fisher's exact test), Q value = 0.84

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	26	21
subtype1	12	12
subtype2	14	9

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.193 (Fisher's exact test), Q value = 0.71

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	40	3	4
subtype1	21	0	3
subtype2	19	3	1

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0996 (Fisher's exact test), Q value = 0.7

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	18	1	28
subtype1	6	1	17
subtype2	12	0	11

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.74 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	35
subtype1	7	17
subtype2	5	18

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S15. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	8	11	13

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0626 (logrank test), Q value = 0.63

Table S16. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	6	0.0 - 211.2 (23.3)
subtype1	8	2	0.0 - 52.0 (13.4)
subtype2	10	3	1.9 - 37.2 (15.7)
subtype3	13	1	12.7 - 211.2 (31.7)

Figure S13. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.803 (Kruskal-Wallis (anova)), Q value = 1

Table S17. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	32	54.8 (14.4)
subtype1	8	55.0 (16.3)
subtype2	11	57.0 (12.9)
subtype3	13	52.8 (15.3)

Figure S14. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.901 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	15	17
subtype1	3	5
subtype2	6	5
subtype3	6	7

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.483 (Fisher's exact test), Q value = 0.84

Table S19. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	26	2	4
subtype1	7	1	0
subtype2	9	1	1
subtype3	10	0	3

Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.298 (Fisher's exact test), Q value = 0.71

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	WHITE
ALL	16	16
subtype1	2	6
subtype2	7	4
subtype3	7	6

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.868 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	25
subtype1	1	7
subtype2	3	8
subtype3	3	10

Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S22. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	6	14	12

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.252 (logrank test), Q value = 0.71

Table S23. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	6	0.0 - 211.2 (23.3)
subtype1	6	1	0.0 - 47.4 (8.6)
subtype2	14	2	12.7 - 211.2 (31.4)
subtype3	11	3	1.9 - 196.6 (18.2)

Figure S19. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.616 (Kruskal-Wallis (anova)), Q value = 0.9

Table S24. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	32	54.8 (14.4)
subtype1	6	55.2 (16.9)
subtype2	14	52.1 (15.3)
subtype3	12	57.8 (12.6)

Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	15	17
subtype1	3	3
subtype2	6	8
subtype3	6	6

Figure S21. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.478 (Fisher's exact test), Q value = 0.84

Table S26. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	26	2	4
subtype1	5	1	0
subtype2	11	0	3
subtype3	10	1	1

Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.241 (Fisher's exact test), Q value = 0.71

Table S27. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	WHITE
ALL	16	16
subtype1	1	5
subtype2	8	6
subtype3	7	5

Figure S23. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	25
subtype1	1	5
subtype2	3	11
subtype3	3	9

Figure S24. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S29. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2
Number of samples	15	13

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.152 (logrank test), Q value = 0.7

Table S30. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	28	6	0.0 - 211.2 (31.8)
subtype1	15	3	0.0 - 211.2 (36.1)
subtype2	13	3	1.9 - 98.1 (24.7)

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.153 (Wilcoxon-test), Q value = 0.7

Table S31. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	28	56.5 (12.3)
subtype1	15	60.1 (9.9)
subtype2	13	52.4 (13.7)

Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.151 (Fisher's exact test), Q value = 0.7

Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	15	13
subtype1	6	9
subtype2	9	4

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.483 (Fisher's exact test), Q value = 0.84

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	25	2	1
subtype1	13	2	0
subtype2	12	0	1

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.124 (Fisher's exact test), Q value = 0.7

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	1	16
subtype1	4	0	11
subtype2	7	1	5

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	27
subtype1	1	14
subtype2	0	13

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S36. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	13	10	5

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0758 (logrank test), Q value = 0.65

Table S37. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	28	6	0.0 - 211.2 (31.8)
subtype1	13	2	4.3 - 211.2 (35.5)
subtype2	10	3	1.9 - 98.1 (22.1)
subtype3	5	1	0.0 - 57.2 (36.1)

Figure S31. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.547 (Kruskal-Wallis (anova)), Q value = 0.84

Table S38. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	28	56.5 (12.3)
subtype1	13	58.8 (12.7)
subtype2	10	54.7 (13.0)
subtype3	5	54.2 (10.9)

Figure S32. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.293 (Fisher's exact test), Q value = 0.71

Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	15	13
subtype1	5	8
subtype2	6	4
subtype3	4	1

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.635 (Fisher's exact test), Q value = 0.91

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	25	2	1
subtype1	11	1	1
subtype2	10	0	0
subtype3	4	1	0

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.524 (Fisher's exact test), Q value = 0.84

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	1	16
subtype1	5	0	8
subtype2	5	1	4
subtype3	1	0	4

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	27
subtype1	1	12
subtype2	0	10
subtype3	0	5

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S43. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	13	11	10	12

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.854 (logrank test), Q value = 1

Table S44. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	45	9	0.0 - 211.2 (27.3)
subtype1	13	3	0.0 - 211.2 (24.7)
subtype2	10	2	1.9 - 89.3 (22.0)
subtype3	10	2	9.8 - 112.2 (19.7)
subtype4	12	2	4.1 - 116.8 (35.8)

Figure S37. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.947 (Kruskal-Wallis (anova)), Q value = 1

Table S45. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	46	56.3 (14.3)
subtype1	13	58.7 (9.2)
subtype2	11	57.5 (12.1)
subtype3	10	55.5 (16.3)
subtype4	12	53.2 (19.2)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.192 (Fisher's exact test), Q value = 0.71

Table S46. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	21
subtype1	6	7
subtype2	8	3
subtype3	3	7
subtype4	8	4

Figure S39. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0594 (Fisher's exact test), Q value = 0.63

Table S47. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	39	3	4
subtype1	10	3	0
subtype2	9	0	2
subtype3	10	0	0
subtype4	10	0	2

Figure S40. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.866 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	18	1	27
subtype1	5	0	8
subtype2	4	1	6
subtype3	5	0	5
subtype4	4	0	8

Figure S41. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.271 (Fisher's exact test), Q value = 0.71

Table S49. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	34
subtype1	2	11
subtype2	3	8
subtype3	5	5
subtype4	2	10

Figure S42. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S50. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	9	12	7	12	6

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.548 (logrank test), Q value = 0.84

Table S51. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	45	9	0.0 - 211.2 (27.3)
subtype1	9	1	4.3 - 211.2 (31.1)
subtype2	11	3	1.9 - 116.8 (22.3)
subtype3	7	2	0.0 - 196.6 (14.0)
subtype4	12	2	9.8 - 112.2 (32.3)
subtype5	6	1	4.1 - 98.1 (31.8)

Figure S43. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.882 (Kruskal-Wallis (anova)), Q value = 1

Table S52. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	46	56.3 (14.3)
subtype1	9	56.3 (10.4)
subtype2	12	58.8 (12.6)
subtype3	7	57.7 (9.8)
subtype4	12	56.2 (18.9)
subtype5	6	49.8 (18.3)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.185 (Fisher's exact test), Q value = 0.71

Table S53. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	21
subtype1	2	7
subtype2	8	4
subtype3	4	3
subtype4	6	6
subtype5	5	1

Figure S45. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.0277 (Fisher's exact test), Q value = 0.55

Table S54. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	39	3	4
subtype1	9	0	0
subtype2	10	0	2
subtype3	4	3	0
subtype4	11	0	1
subtype5	5	0	1

Figure S46. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.976 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	18	1	27
subtype1	4	0	5
subtype2	4	1	7
subtype3	3	0	4
subtype4	4	0	8
subtype5	3	0	3

Figure S47. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0164 (Fisher's exact test), Q value = 0.49

Table S56. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	34
subtype1	0	9
subtype2	3	9
subtype3	2	5
subtype4	7	5
subtype5	0	6

Figure S48. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S57. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2
Number of samples	18	23

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.818 (logrank test), Q value = 1

Table S58. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	40	9	0.0 - 211.2 (27.3)
subtype1	18	4	0.0 - 211.2 (24.0)
subtype2	22	5	1.9 - 116.8 (36.7)

Figure S49. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.511 (Wilcoxon-test), Q value = 0.84

Table S59. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	41	55.1 (14.1)
subtype1	18	56.9 (12.7)
subtype2	23	53.7 (15.3)

Figure S50. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.355 (Fisher's exact test), Q value = 0.76

Table S60. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	22	19
subtype1	8	10
subtype2	14	9

Figure S51. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0366 (Fisher's exact test), Q value = 0.55

Table S61. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	35	3	3
subtype1	15	3	0
subtype2	20	0	3

Figure S52. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.33 (Fisher's exact test), Q value = 0.73

Table S62. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	16	1	24
subtype1	9	0	9
subtype2	7	1	15

Figure S53. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.724 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	31
subtype1	5	13
subtype2	5	18

Figure S54. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S64. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	3	5	6	6	8	3	5	2	3

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.918 (logrank test), Q value = 1

Table S65. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	38	8	0.0 - 211.2 (26.7)
subtype1	3	0	4.3 - 22.3 (14.0)
subtype2	4	1	1.9 - 42.7 (11.3)
subtype3	6	2	0.6 - 196.6 (24.6)
subtype4	6	1	10.3 - 38.2 (19.7)
subtype5	8	2	4.1 - 211.2 (29.2)
subtype6	3	1	35.5 - 116.8 (52.0)
subtype7	5	1	0.0 - 43.9 (32.0)
subtype9	3	0	37.2 - 112.2 (89.3)

Figure S55. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.754 (Kruskal-Wallis (anova)), Q value = 1

Table S66. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	39	55.5 (14.3)
subtype1	3	61.0 (13.5)
subtype2	5	58.0 (14.2)
subtype3	6	47.2 (19.7)
subtype4	6	62.0 (10.3)
subtype5	8	51.6 (12.1)
subtype6	3	54.7 (23.1)
subtype7	5	60.0 (6.3)
subtype9	3	53.0 (21.2)

Figure S56. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.47 (Fisher's exact test), Q value = 0.84

Table S67. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	18
subtype1	2	1
subtype2	3	2
subtype3	3	3
subtype4	2	4
subtype5	4	4
subtype6	1	2
subtype7	5	0
subtype9	1	2

Figure S57. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.24 (Fisher's exact test), Q value = 0.71

Table S68. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients	DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL)	PRIMARY DLBCL OF THE CNS	PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL	33	3	3
subtype1	3	0	0
subtype2	4	0	1
subtype3	3	2	1
subtype4	6	0	0
subtype5	8	0	0
subtype6	2	0	1
subtype7	4	1	0
subtype9	3	0	0

Figure S58. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.119 (Fisher's exact test), Q value = 0.7

Table S69. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RACE'

nPatients	ASIAN	WHITE
ALL	16	23
subtype1	2	1
subtype2	1	4
subtype3	1	5
subtype4	4	2
subtype5	6	2
subtype6	1	2
subtype7	1	4
subtype9	0	3

Figure S59. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.00209 (Fisher's exact test), Q value = 0.13

Table S70. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	29
subtype1	0	3
subtype2	1	4
subtype3	4	2
subtype4	2	4
subtype5	0	8
subtype6	0	3
subtype7	0	5
subtype9	3	0

Figure S60. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/DLBC-TP/15092398/DLBC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/DLBC-TP/15078461/DLBC-TP.merged_data.txt
Number of patients = 47
Number of clustering approaches = 10
Number of selected clinical features = 6
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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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)

[5] 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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