Correlation between aggregated molecular cancer subtypes and selected clinical features

Mesothelioma (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/C1MG7N57

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 6 different clustering approaches and 7 clinical features across 13 patients, no 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.
2 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
2 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.
2 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 6 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.0414 (1.00)	0.237 (1.00)	0.11 (1.00)	0.00673 (0.283)	0.331 (1.00)	0.331 (1.00)
AGE	t-test	0.391 (1.00)	0.628 (1.00)	0.178 (1.00)	0.109 (1.00)	0.643 (1.00)	0.643 (1.00)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.529 (1.00)	0.237 (1.00)	0.464 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.266 (1.00)	0.266 (1.00)	0.0476 (1.00)	0.143 (1.00)	0.206 (1.00)	0.206 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	1 (1.00)	1 (1.00)	0.5 (1.00)	0.429 (1.00)	0.444 (1.00)	0.444 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.559 (1.00)	0.559 (1.00)	0.5 (1.00)	0.429 (1.00)	1 (1.00)	1 (1.00)
GENDER	Fisher's exact test	1 (1.00)	1 (1.00)	0.464 (1.00)	1 (1.00)	0.167 (1.00)	0.167 (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
Number of samples	6	7

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

P value = 0.0414 (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	13	10	1.9 - 91.7 (18.8)
subtype1	6	5	1.9 - 23.3 (15.5)
subtype2	7	5	5.2 - 91.7 (43.3)

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 = 0.391 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	13	61.1 (8.0)
subtype1	6	59.0 (5.3)
subtype2	7	62.9 (9.8)

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 = 0.529 (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
ALL	1	5	7
subtype1	0	2	4
subtype2	1	3	3

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 = 0.266 (Fisher's exact 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
ALL	9	4
subtype1	3	3
subtype2	6	1

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

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

nPatients	0	1
ALL	10	3
subtype1	5	1
subtype2	5	2

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 'PATHOLOGY.M.STAGE'

P value = 0.559 (Fisher's exact test), Q value = 1

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

nPatients	M0	MX
ALL	9	4
subtype1	5	1
subtype2	4	3

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	4	9
subtype1	2	4
subtype2	2	5

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

Clustering Approach #2: 'METHLYATION CNMF'

Table S9. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2
Number of samples	7	6

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.237 (logrank test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	13	10	1.9 - 91.7 (18.8)
subtype1	7	5	13.6 - 91.7 (23.3)
subtype2	6	5	1.9 - 80.8 (8.1)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.628 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	13	61.1 (8.0)
subtype1	7	60.0 (7.4)
subtype2	6	62.3 (9.1)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.237 (Chi-square test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III
ALL	1	5	7
subtype1	0	4	3
subtype2	1	1	4

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.266 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3
ALL	9	4
subtype1	6	1
subtype2	3	3

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

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

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	10	3
subtype1	5	2
subtype2	5	1

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.559 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	9	4
subtype1	4	3
subtype2	5	1

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

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

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	4	9
subtype1	2	5
subtype2	2	4

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

Clustering Approach #3: 'MIRSEQ CNMF'

Table S17. Description of clustering approach #3: 'MIRSEQ CNMF'

Cluster Labels	1	2
Number of samples	6	3

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.11 (logrank test), Q value = 1

Table S18. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	9	7	1.9 - 51.8 (13.6)
subtype1	6	4	1.9 - 51.8 (16.2)
subtype2	3	3	3.5 - 17.3 (5.2)

Figure S15. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.178 (t-test), Q value = 1

Table S19. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	9	61.6 (8.1)
subtype1	6	59.5 (9.4)
subtype2	3	65.7 (2.3)

Figure S16. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.464 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III
ALL	3	6
subtype1	3	3
subtype2	0	3

Figure S17. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0476 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3
ALL	5	4
subtype1	5	1
subtype2	0	3

Figure S18. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.5 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	7	2
subtype1	4	2
subtype2	3	0

Figure S19. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.5 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	7	2
subtype1	4	2
subtype2	3	0

Figure S20. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.464 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	3	6
subtype1	3	3
subtype2	0	3

Figure S21. Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

Clustering Approach #4: 'MIRSEQ CHIERARCHICAL'

Table S25. Description of clustering approach #4: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	4	4	1

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00673 (logrank test), Q value = 0.28

Table S26. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	8	6	1.9 - 51.8 (12.3)
subtype1	4	4	1.9 - 10.9 (4.4)
subtype2	4	2	13.6 - 51.8 (21.1)

Figure S22. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.109 (t-test), Q value = 1

Table S27. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	8	60.9 (8.4)
subtype1	4	66.0 (9.2)
subtype2	4	55.8 (3.0)

Figure S23. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

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

Table S28. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III
ALL	3	5
subtype1	1	3
subtype2	2	2

Figure S24. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.143 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3
ALL	5	3
subtype1	1	3
subtype2	4	0

Figure S25. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.429 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	6	2
subtype1	4	0
subtype2	2	2

Figure S26. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.429 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	6	2
subtype1	4	0
subtype2	2	2

Figure S27. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S32. Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	3	5
subtype1	1	3
subtype2	2	2

Figure S28. Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

Clustering Approach #5: 'MIRseq Mature CNMF subtypes'

Table S33. Description of clustering approach #5: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2
Number of samples	5	4

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

P value = 0.331 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	9	7	1.9 - 51.8 (13.6)
subtype1	5	4	1.9 - 51.8 (18.8)
subtype2	4	3	3.5 - 17.3 (9.4)

Figure S29. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.643 (t-test), Q value = 1

Table S35. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	9	61.6 (8.1)
subtype1	5	60.4 (10.2)
subtype2	4	63.0 (5.7)

Figure S30. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S36. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III
ALL	3	6
subtype1	2	3
subtype2	1	3

Figure S31. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.206 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3
ALL	5	4
subtype1	4	1
subtype2	1	3

Figure S32. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.444 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	7	2
subtype1	3	2
subtype2	4	0

Figure S33. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

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

Table S39. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	7	2
subtype1	4	1
subtype2	3	1

Figure S34. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.167 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	3	6
subtype1	3	2
subtype2	0	4

Figure S35. Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

Clustering Approach #6: 'MIRseq Mature cHierClus subtypes'

Table S41. Description of clustering approach #6: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2
Number of samples	4	5

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

P value = 0.331 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	9	7	1.9 - 51.8 (13.6)
subtype1	4	3	3.5 - 17.3 (9.4)
subtype2	5	4	1.9 - 51.8 (18.8)

Figure S36. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.643 (t-test), Q value = 1

Table S43. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	9	61.6 (8.1)
subtype1	4	63.0 (5.7)
subtype2	5	60.4 (10.2)

Figure S37. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S44. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE II	STAGE III
ALL	3	6
subtype1	1	3
subtype2	2	3

Figure S38. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.206 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3
ALL	5	4
subtype1	1	3
subtype2	4	1

Figure S39. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.444 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	7	2
subtype1	4	0
subtype2	3	2

Figure S40. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

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

Table S47. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	7	2
subtype1	3	1
subtype2	4	1

Figure S41. Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.167 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	3	6
subtype1	0	4
subtype2	3	2

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

Methods & Data

Input

Cluster data file = MESO-TP.mergedcluster.txt
Clinical data file = MESO-TP.merged_data.txt
Number of patients = 13
Number of clustering approaches = 6
Number of selected clinical features = 7
Exclude small clusters that include fewer than K patients, K = 3

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

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

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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