Correlation between aggregated molecular cancer subtypes and selected clinical features

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 7 clinical features across 25 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.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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 5 subtypes that do not correlate to any clinical features.
3 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.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 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 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	Time to Death	AGE	GENDER	DISTANT METASTASIS	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Chi-square test	Chi-square test	ANOVA	Chi-square test
Copy Number Ratio CNMF subtypes	100 (1.00)	0.164 (1.00)	1 (1.00)		0.085 (1.00)		0.487 (1.00)
METHLYATION CNMF	100 (1.00)	0.463 (1.00)	0.846 (1.00)		0.128 (1.00)		0.34 (1.00)
RNAseq CNMF subtypes	100 (1.00)	0.76 (1.00)	0.841 (1.00)		0.177 (1.00)		0.39 (1.00)
RNAseq cHierClus subtypes	100 (1.00)	0.395 (1.00)	0.653 (1.00)		0.327 (1.00)		0.559 (1.00)
MIRSEQ CNMF	100 (1.00)	0.127 (1.00)	0.597 (1.00)		0.225 (1.00)		0.16 (1.00)
MIRSEQ CHIERARCHICAL	100 (1.00)	0.715 (1.00)	0.604 (1.00)		0.286 (1.00)		0.56 (1.00)
MIRseq Mature CNMF subtypes	100 (1.00)	0.785 (1.00)	0.236 (1.00)		0.313 (1.00)		0.127 (1.00)
MIRseq Mature cHierClus subtypes	100 (1.00)	0.494 (1.00)	0.588 (1.00)		0.492 (1.00)		0.265 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	1	19	5

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

P value = 100 (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	23	2	0.6 - 142.5 (51.0)
subtype2	18	2	0.6 - 142.5 (51.6)
subtype3	5	0	2.5 - 89.7 (20.9)

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 = 0.164 (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	24	51.8 (14.6)
subtype2	19	49.3 (13.8)
subtype3	5	61.2 (15.2)

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 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	10	14
subtype2	8	11
subtype3	2	3

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 'LYMPH.NODE.METASTASIS'

P value = 0.085 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	9
subtype2	12	2	5
subtype3	1	0	4

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.487 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	11	2	3
subtype2	7	8	1	3
subtype3	1	3	1	0

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S7. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	4	16	5

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	4	0	23.5 - 80.4 (54.8)
subtype2	15	2	2.5 - 142.5 (52.3)
subtype3	5	0	0.6 - 75.7 (11.2)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.463 (ANOVA), Q value = 1

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	4	57.5 (11.8)
subtype2	16	53.1 (16.1)
subtype3	5	45.6 (10.4)

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.846 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	11	14
subtype1	1	3
subtype2	8	8
subtype3	2	3

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.128 (Chi-square test), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	3	0	1
subtype2	10	1	5
subtype3	0	1	4

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	1	3	0	0
subtype2	4	9	1	2
subtype3	3	0	1	1

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	3	11	7	4

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	3	0	37.0 - 89.7 (65.2)
subtype2	10	0	2.5 - 142.5 (24.5)
subtype3	7	2	20.9 - 114.2 (67.8)
subtype4	4	0	0.6 - 75.7 (29.6)

Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.76 (ANOVA), Q value = 1

Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	3	55.3 (11.9)
subtype2	11	52.2 (17.5)
subtype3	7	55.1 (13.1)
subtype4	4	45.5 (12.0)

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.841 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	11	14
subtype1	2	1
subtype2	5	6
subtype3	3	4
subtype4	1	3

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

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.177 (Chi-square test), Q value = 1

Table S17. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	1	0	2
subtype2	7	0	4
subtype3	5	1	1
subtype4	0	1	3

Figure S14. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.39 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	1	2	0	0
subtype2	4	5	1	1
subtype3	0	5	1	1
subtype4	3	0	0	1

Figure S15. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S19. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

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

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	5	2	28.1 - 114.2 (67.8)
subtype2	5	0	0.6 - 75.7 (23.5)
subtype3	8	0	3.5 - 142.5 (25.7)
subtype4	3	0	2.5 - 87.9 (20.9)
subtype5	3	0	37.0 - 89.7 (65.2)

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.395 (ANOVA), Q value = 1

Table S21. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	5	55.4 (15.1)
subtype2	5	45.0 (10.5)
subtype3	9	49.4 (14.9)
subtype4	3	65.0 (19.7)
subtype5	3	55.3 (11.9)

Figure S17. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.653 (Chi-square test), Q value = 1

Table S22. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	11	14
subtype1	2	3
subtype2	1	4
subtype3	4	5
subtype4	2	1
subtype5	2	1

Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.327 (Chi-square test), Q value = 1

Table S23. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	4	1	0
subtype2	1	1	3
subtype3	6	0	3
subtype4	1	0	2
subtype5	1	0	2

Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.559 (Chi-square test), Q value = 1

Table S24. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	0	3	1	1
subtype2	3	1	0	1
subtype3	4	3	1	1
subtype4	0	3	0	0
subtype5	1	2	0	0

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S25. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	9	8	8

'MIRSEQ CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S26. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	8	2	0.6 - 142.5 (39.5)
subtype2	8	0	3.5 - 102.8 (51.1)
subtype3	8	0	11.2 - 114.2 (46.9)

Figure S21. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.127 (ANOVA), Q value = 1

Table S27. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	9	58.4 (17.9)
subtype2	8	53.5 (9.1)
subtype3	8	44.2 (12.5)

Figure S22. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.597 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	11	14
subtype1	3	6
subtype2	3	5
subtype3	5	3

Figure S23. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.225 (Chi-square test), Q value = 1

Table S29. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	3	2	4
subtype2	4	0	4
subtype3	6	0	2

Figure S24. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.16 (Chi-square test), Q value = 1

Table S30. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	4	3	0	2
subtype2	1	6	0	1
subtype3	3	3	2	0

Figure S25. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S31. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2
Number of samples	4	21

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S32. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	4	1	0.6 - 75.7 (30.2)
subtype2	20	1	2.5 - 142.5 (44.0)

Figure S26. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.715 (t-test), Q value = 1

Table S33. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	4	49.2 (17.5)
subtype2	21	52.9 (14.4)

Figure S27. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.604 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	11	14
subtype1	1	3
subtype2	10	11

Figure S28. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.286 (Chi-square test), Q value = 1

Table S35. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	1	1	2
subtype2	12	1	8

Figure S29. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.56 (Chi-square test), Q value = 1

Table S36. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	2	1	0	1
subtype2	6	11	2	2

Figure S30. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S37. Get Full Table Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	10	7	8

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

P value = 100 (logrank test), Q value = 1

Table S38. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	2	0.6 - 142.5 (44.0)
subtype1	10	0	2.5 - 102.8 (25.7)
subtype2	7	0	11.2 - 114.2 (67.8)
subtype3	7	2	0.6 - 142.5 (51.0)

Figure S31. 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 = 0.785 (ANOVA), Q value = 1

Table S39. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	52.3 (14.6)
subtype1	10	52.1 (15.7)
subtype2	7	49.6 (13.3)
subtype3	8	55.0 (15.6)

Figure S32. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.236 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	11	14
subtype1	4	6
subtype2	5	2
subtype3	2	6

Figure S33. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.313 (Chi-square test), Q value = 1

Table S41. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	13	2	10
subtype1	6	0	4
subtype2	4	0	3
subtype3	3	2	3

Figure S34. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.127 (Chi-square test), Q value = 1

Table S42. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	8	12	2	3
subtype1	3	6	0	1
subtype2	1	4	2	0
subtype3	4	2	0	2

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

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S43. Get Full Table Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

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

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

P value = 100 (logrank test), Q value = 1

Table S44. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	21	2	0.6 - 142.5 (51.0)
subtype2	6	1	23.4 - 142.5 (74.1)
subtype4	4	1	0.6 - 75.7 (30.2)
subtype5	7	0	2.5 - 102.8 (25.9)
subtype6	4	0	23.5 - 72.7 (38.2)

Figure S36. 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 = 0.494 (ANOVA), Q value = 1

Table S45. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	21	51.4 (14.6)
subtype2	6	49.2 (15.0)
subtype4	4	49.2 (17.5)
subtype5	7	58.3 (14.1)
subtype6	4	45.0 (12.6)

Figure S37. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.588 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	8	13
subtype2	4	2
subtype4	1	3
subtype5	2	5
subtype6	1	3

Figure S38. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.492 (Chi-square test), Q value = 1

Table S47. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	NX
ALL	11	2	8
subtype2	4	1	1
subtype4	1	1	2
subtype5	3	0	4
subtype6	3	0	1

Figure S39. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.265 (Chi-square test), Q value = 1

Table S48. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	6	11	1	3
subtype2	0	4	1	1
subtype4	2	1	0	1
subtype5	1	5	0	1
subtype6	3	1	0	0

Figure S40. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = KICH-TP.mergedcluster.txt
Clinical data file = KICH-TP.clin.merged.picked.txt
Number of patients = 25
Number of clustering approaches = 8
Number of selected clinical features = 7
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

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

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

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

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

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.

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

[5] 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)

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

[7] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[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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