Correlation between aggregated molecular cancer subtypes and selected clinical features

Adrenocortical Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HX1B84

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 8 different clustering approaches and 6 clinical features across 34 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.N.STAGE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 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 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes 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 8 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, 3 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.0844 (1.00)	0.196 (1.00)	0.303 (1.00)	0.721 (1.00)	0.000174 (0.00835)	0.263 (1.00)
METHLYATION CNMF	0.0281 (1.00)	0.806 (1.00)	0.592 (1.00)	0.742 (1.00)	0.18 (1.00)	0.000689 (0.0324)
RNAseq CNMF subtypes	0.00759 (0.342)	0.293 (1.00)	0.468 (1.00)	0.34 (1.00)	0.0951 (1.00)	0.042 (1.00)
RNAseq cHierClus subtypes	0.0194 (0.816)	0.26 (1.00)	0.166 (1.00)	0.26 (1.00)	0.014 (0.604)	0.00391 (0.18)
MIRSEQ CNMF	0.0493 (1.00)	0.273 (1.00)	0.108 (1.00)	0.0393 (1.00)	0.122 (1.00)	0.021 (0.861)
MIRSEQ CHIERARCHICAL	0.536 (1.00)	0.0754 (1.00)	0.0454 (1.00)	0.0848 (1.00)	0.327 (1.00)	0.151 (1.00)
MIRseq Mature CNMF subtypes	0.119 (1.00)	0.399 (1.00)	0.0721 (1.00)	0.0592 (1.00)	0.223 (1.00)	0.0354 (1.00)
MIRseq Mature cHierClus subtypes	0.536 (1.00)	0.0137 (0.602)	0.0454 (1.00)	0.0848 (1.00)	0.327 (1.00)	0.355 (1.00)

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

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

Cluster Labels	1	2	3	4	5
Number of samples	10	9	3	8	3

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

P value = 0.0844 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	33	8	6.9 - 121.2 (29.2)
subtype1	10	4	8.3 - 50.7 (23.9)
subtype2	9	0	6.9 - 121.2 (41.3)
subtype3	3	1	10.2 - 23.3 (17.8)
subtype4	8	3	11.3 - 63.2 (28.8)
subtype5	3	0	22.2 - 73.0 (46.8)

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 'AGE'

P value = 0.196 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	33	51.1 (14.5)
subtype1	10	46.9 (17.6)
subtype2	9	55.9 (8.7)
subtype3	3	59.7 (6.1)
subtype4	8	53.1 (11.9)
subtype5	3	36.7 (22.0)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.303 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	1	3	2	3
subtype2	1	5	2	1
subtype3	0	0	0	3
subtype4	1	3	0	1
subtype5	1	2	0	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.721 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	1	4	4
subtype2	1	6	2
subtype3	0	1	2
subtype4	1	3	1
subtype5	1	2	0

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.000174 (Chi-square test), Q value = 0.0084

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

nPatients	0	1
ALL	26	4
subtype1	8	1
subtype2	9	0
subtype3	0	3
subtype4	6	0
subtype5	3	0

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.263 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	17	16
subtype1	8	2
subtype2	3	6
subtype3	1	2
subtype4	4	4
subtype5	1	2

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5
Number of samples	6	2	12	11	3

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0281 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	32	8	6.9 - 121.2 (28.3)
subtype1	6	4	8.3 - 33.8 (18.3)
subtype3	12	3	10.2 - 106.5 (36.9)
subtype4	11	1	6.9 - 121.2 (29.2)
subtype5	3	0	12.6 - 22.2 (22.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.806 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	32	50.1 (14.4)
subtype1	6	52.3 (19.0)
subtype3	12	50.8 (12.5)
subtype4	11	50.3 (13.5)
subtype5	3	42.3 (20.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.592 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	11	4	8
subtype1	0	2	2	2
subtype3	1	2	1	4
subtype4	2	5	1	2
subtype5	1	2	0	0

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

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.742 (Chi-square test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	14	9
subtype1	0	3	3
subtype3	1	4	3
subtype4	2	5	3
subtype5	1	2	0

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.18 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	24	4
subtype1	5	1
subtype3	6	3
subtype4	10	0
subtype5	3	0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.000689 (Fisher's exact test), Q value = 0.032

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

nPatients	FEMALE	MALE
ALL	15	17
subtype1	6	0
subtype3	2	10
subtype4	4	7
subtype5	3	0

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	9	4	10	11

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00759 (logrank test), Q value = 0.34

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	9	5	8.3 - 37.1 (18.1)
subtype2	4	0	10.2 - 41.4 (27.3)
subtype3	10	3	17.4 - 106.5 (46.6)
subtype4	11	0	6.9 - 121.2 (29.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.293 (ANOVA), Q value = 1

Table S17. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	9	54.1 (15.6)
subtype2	4	59.2 (9.2)
subtype3	10	51.1 (10.3)
subtype4	11	44.8 (16.8)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.468 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	0	4	2	3
subtype2	0	1	1	2
subtype3	1	3	1	2
subtype4	3	5	0	1

Figure S15. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.34 (Chi-square test), Q value = 1

Table S19. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	0	5	4
subtype2	0	3	1
subtype3	1	3	3
subtype4	3	5	1

Figure S16. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0951 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	8	1
subtype2	2	2
subtype3	6	1
subtype4	10	0

Figure S17. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.042 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	8	1
subtype2	2	2
subtype3	3	7
subtype4	4	7

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	8	17	9

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0194 (logrank test), Q value = 0.82

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	8	4	8.3 - 37.1 (18.3)
subtype2	17	1	6.9 - 121.2 (29.2)
subtype3	9	3	10.2 - 63.2 (31.2)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.26 (ANOVA), Q value = 1

Table S24. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	8	54.4 (16.7)
subtype2	17	46.8 (14.7)
subtype3	9	55.3 (10.1)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.166 (Chi-square test), Q value = 1

Table S25. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	0	4	2	2
subtype2	4	7	1	2
subtype3	0	2	1	4

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.26 (Chi-square test), Q value = 1

Table S26. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	0	5	3
subtype2	4	7	3
subtype3	0	4	3

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.014 (Fisher's exact test), Q value = 0.6

Table S27. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	7	1
subtype2	15	0
subtype3	4	3

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00391 (Fisher's exact test), Q value = 0.18

Table S28. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	8	0
subtype2	6	11
subtype3	3	6

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	13	7	14

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0493 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	13	4	10.1 - 106.5 (22.0)
subtype2	7	3	8.3 - 42.5 (18.5)
subtype3	14	1	6.9 - 121.2 (48.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.273 (ANOVA), Q value = 1

Table S31. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	13	51.1 (13.0)
subtype2	7	57.9 (15.8)
subtype3	14	47.1 (14.4)

Figure S26. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.108 (Chi-square test), Q value = 1

Table S32. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	1	3	2	5
subtype2	0	2	1	3
subtype3	3	8	1	0

Figure S27. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0393 (Chi-square test), Q value = 1

Table S33. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	1	4	6
subtype2	0	3	3
subtype3	3	9	0

Figure S28. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.122 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	8	3
subtype2	5	1
subtype3	13	0

Figure S29. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.021 (Fisher's exact test), Q value = 0.86

Table S35. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	9	4
subtype2	5	2
subtype3	3	11

Figure S30. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S36. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	14	5	15

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.536 (logrank test), Q value = 1

Table S37. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	14	3	6.9 - 121.2 (30.6)
subtype2	5	1	29.2 - 93.4 (46.8)
subtype3	15	4	8.3 - 106.5 (22.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0754 (ANOVA), Q value = 1

Table S38. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	14	53.9 (14.5)
subtype2	5	37.6 (13.9)
subtype3	15	52.4 (12.6)

Figure S32. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0454 (Chi-square test), Q value = 1

Table S39. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	1	9	0	3
subtype2	2	1	1	0
subtype3	1	3	3	5

Figure S33. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.0848 (Chi-square test), Q value = 1

Table S40. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	1	9	3
subtype2	2	2	0
subtype3	1	5	6

Figure S34. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.327 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	13	1
subtype2	4	0
subtype3	9	3

Figure S35. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.151 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	6	8
subtype2	1	4
subtype3	10	5

Figure S36. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S43. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	14	7	13

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

P value = 0.119 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	14	4	8.3 - 106.5 (19.9)
subtype2	7	3	18.1 - 63.2 (33.8)
subtype3	13	1	6.9 - 121.2 (46.8)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.399 (ANOVA), Q value = 1

Table S45. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	14	51.6 (12.7)
subtype2	7	56.1 (16.3)
subtype3	13	47.1 (15.0)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0721 (Chi-square test), Q value = 1

Table S46. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	1	3	3	5
subtype2	0	3	0	3
subtype3	3	7	1	0

Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0592 (Chi-square test), Q value = 1

Table S47. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	1	5	6
subtype2	0	3	3
subtype3	3	8	0

Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.223 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	9	3
subtype2	5	1
subtype3	12	0

Figure S41. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0354 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	10	4
subtype2	4	3
subtype3	3	10

Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S50. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	14	6	14

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

P value = 0.536 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	34	8	6.9 - 121.2 (29.8)
subtype1	14	4	8.3 - 106.5 (22.7)
subtype2	6	1	10.1 - 93.4 (39.0)
subtype3	14	3	6.9 - 121.2 (30.6)

Figure S43. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0137 (ANOVA), Q value = 0.6

Table S52. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	34	50.8 (14.3)
subtype1	14	54.2 (10.8)
subtype2	6	35.8 (13.1)
subtype3	14	53.9 (14.5)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0454 (Chi-square test), Q value = 1

Table S53. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	4	13	4	8
subtype1	1	3	3	5
subtype2	2	1	1	0
subtype3	1	9	0	3

Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0848 (Chi-square test), Q value = 1

Table S54. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2+T3	T4
ALL	4	16	9
subtype1	1	5	6
subtype2	2	2	0
subtype3	1	9	3

Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.327 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	26	4
subtype1	9	3
subtype2	4	0
subtype3	13	1

Figure S47. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.355 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	17	17
subtype1	9	5
subtype2	2	4
subtype3	6	8

Figure S48. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

Methods & Data

Input

Cluster data file = ACC-TP.mergedcluster.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 34
Number of clustering approaches = 8
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

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

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] 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] 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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