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 4 different clustering approaches and 12 clinical features across 22 patients, 17 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'TUMOR_TISSUE_SITE',  'PATHOLOGY_T_STAGE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'TUMOR_TISSUE_SITE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE',  'PATHOLOGY_T_STAGE',  'GENDER',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'TUMOR_TISSUE_SITE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 17 significant findings detected.

Clinical
Features 		Statistical
Tests 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.0123
(0.0455) 		0.323
(0.553) 		0.259
(0.46) 		0.0407
(0.115)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.782
(1.00) 		0.69
(1.00) 		0.355
(0.588) 		0.783
(1.00)
TUMOR TISSUE SITE Fisher's exact test 1e-05
(6e-05) 		1e-05
(6e-05) 		1e-05
(6e-05) 		1e-05
(6e-05)
PATHOLOGIC STAGE Fisher's exact test 0.15
(0.326) 		0.2
(0.399) 		0.199
(0.399) 		0.0666
(0.178)
PATHOLOGY T STAGE Fisher's exact test 0.018
(0.0617) 		0.132
(0.304) 		0.0356
(0.107) 		0.242
(0.446)
PATHOLOGY N STAGE Fisher's exact test 1
(1.00) 		1
(1.00) 		0.595
(0.892) 		1
(1.00)
GENDER Fisher's exact test 0.00014
(0.000747) 		0.00364
(0.0173) 		0.00397
(0.0173) 		0.00681
(0.0272)
RADIATION THERAPY Fisher's exact test 0.217
(0.416) 		0.0839
(0.212) 		0.0227
(0.0725) 		0.133
(0.304)
HISTOLOGICAL TYPE Fisher's exact test 1e-05
(6e-05) 		1e-05
(6e-05) 		1e-05
(6e-05) 		1e-05
(6e-05)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova)
NUMBER OF LYMPH NODES Fisher's exact test 1
(1.00) 		0.839
(1.00) 		1
(1.00) 		0.836
(1.00)
RACE Fisher's exact test 1
(1.00) 		0.462
(0.739) 		0.748
(1.00) 		0.534
(0.827)
Clustering Approach #1: 'MIRSEQ CNMF'

Table S1.  Description of clustering approach #1: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 5 10 4 3
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0123 (logrank test), Q value = 0.046

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

nPatients nDeath Duration Range (Median), Month
ALL 22 3 2.7 - 40.1 (21.9)
subtype1 5 0 9.9 - 32.9 (20.1)
subtype2 10 1 10.2 - 35.0 (26.0)
subtype3 4 0 10.4 - 35.9 (22.2)
subtype4 3 2 2.7 - 40.1 (7.3)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.782 (Kruskal-Wallis (anova)), Q value = 1

Table S3.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 22 63.9 (9.5)
subtype1 5 64.8 (12.9)
subtype2 10 62.4 (9.4)
subtype3 4 65.5 (9.3)
subtype4 3 65.0 (8.0)

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

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S4.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients BLADDER BREAST COLON ENDOMETRIAL KIDNEY LUNG
ALL 3 5 4 3 4 3
subtype1 0 0 4 0 0 1
subtype2 0 5 0 3 0 2
subtype3 0 0 0 0 4 0
subtype4 3 0 0 0 0 0

Figure S3.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.15 (Fisher's exact test), Q value = 0.33

Table S5.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIC STAGE IV
ALL 5 1 1 1 5 2 1 1 1 1
subtype1 1 0 0 0 2 1 0 0 1 0
subtype2 0 1 1 0 3 1 0 1 0 0
subtype3 3 0 0 1 0 0 0 0 0 0
subtype4 1 0 0 0 0 0 1 0 0 1

Figure S4.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.018 (Fisher's exact test), Q value = 0.062

Table S6.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 5 7 7
subtype1 0 1 4
subtype2 1 5 1
subtype3 3 1 0
subtype4 1 0 2

Figure S5.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

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

Table S7.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 10 4
subtype1 4 1
subtype2 4 2
subtype3 1 0
subtype4 1 1

Figure S6.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00014 (Fisher's exact test), Q value = 0.00075

Table S8.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 12 10
subtype1 1 4
subtype2 10 0
subtype3 0 4
subtype4 1 2

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.217 (Fisher's exact test), Q value = 0.42

Table S9.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 13 5
subtype1 5 0
subtype2 5 5
subtype3 2 0
subtype4 1 0

Figure S8.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S10.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA KIDNEY CLEAR CELL RENAL CARCINOMA LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG MUCINOUS ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID MUSCLE INVASIVE UROTHELIAL CARCINOMA (PT2 OR ABOVE) SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 2 2 4 1 4 2 1 1 2 2
subtype1 2 2 0 0 0 0 1 0 0 0
subtype2 0 0 4 1 0 2 0 1 0 2
subtype3 0 0 0 0 4 0 0 0 0 0
subtype4 0 0 0 0 0 0 0 0 2 0

Figure S9.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

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

Table S11.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients 0 2
ALL 9 3
subtype1 3 1
subtype2 4 1
subtype3 0 0
subtype4 2 1

Figure S10.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

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

Table S12.  Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 3 19
subtype1 1 4
subtype2 2 8
subtype3 0 4
subtype4 0 3

Figure S11.  Get High-res Image Clustering Approach #1: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #2: 'MIRSEQ CHIERARCHICAL'

Table S13.  Description of clustering approach #2: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 5 5 3 5 4
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.323 (logrank test), Q value = 0.55

Table S14.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 3 2.7 - 40.1 (21.9)
subtype1 5 0 9.9 - 32.9 (20.1)
subtype2 5 2 2.7 - 40.1 (23.2)
subtype3 3 0 20.6 - 33.1 (31.4)
subtype4 5 1 10.2 - 35.0 (24.1)
subtype5 4 0 10.4 - 35.9 (22.2)

Figure S12.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.69 (Kruskal-Wallis (anova)), Q value = 1

Table S15.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 22 63.9 (9.5)
subtype1 5 64.8 (12.9)
subtype2 5 66.8 (6.4)
subtype3 3 58.7 (11.2)
subtype4 5 61.8 (9.8)
subtype5 4 65.5 (9.3)

Figure S13.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S16.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients BLADDER BREAST COLON ENDOMETRIAL KIDNEY LUNG
ALL 3 5 4 3 4 3
subtype1 0 0 4 0 0 1
subtype2 3 0 0 0 0 2
subtype3 0 3 0 0 0 0
subtype4 0 2 0 3 0 0
subtype5 0 0 0 0 4 0

Figure S14.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.2 (Fisher's exact test), Q value = 0.4

Table S17.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIC STAGE IV
ALL 5 1 1 1 5 2 1 1 1 1
subtype1 1 0 0 0 2 1 0 0 1 0
subtype2 1 1 1 0 0 0 1 0 0 1
subtype3 0 0 0 0 2 0 0 1 0 0
subtype4 0 0 0 0 1 1 0 0 0 0
subtype5 3 0 0 1 0 0 0 0 0 0

Figure S15.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.132 (Fisher's exact test), Q value = 0.3

Table S18.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 5 7 7
subtype1 0 1 4
subtype2 2 1 2
subtype3 0 2 1
subtype4 0 2 0
subtype5 3 1 0

Figure S16.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

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

Table S19.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 10 4
subtype1 4 1
subtype2 2 1
subtype3 2 1
subtype4 1 1
subtype5 1 0

Figure S17.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00364 (Fisher's exact test), Q value = 0.017

Table S20.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 12 10
subtype1 1 4
subtype2 3 2
subtype3 3 0
subtype4 5 0
subtype5 0 4

Figure S18.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0839 (Fisher's exact test), Q value = 0.21

Table S21.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 13 5
subtype1 5 0
subtype2 3 0
subtype3 1 2
subtype4 2 3
subtype5 2 0

Figure S19.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S22.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA KIDNEY CLEAR CELL RENAL CARCINOMA LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG MUCINOUS ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID MUSCLE INVASIVE UROTHELIAL CARCINOMA (PT2 OR ABOVE) SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 2 2 4 1 4 2 1 1 2 2
subtype1 2 2 0 0 0 0 1 0 0 0
subtype2 0 0 0 0 0 2 0 0 2 0
subtype3 0 0 2 1 0 0 0 0 0 0
subtype4 0 0 2 0 0 0 0 1 0 2
subtype5 0 0 0 0 4 0 0 0 0 0

Figure S20.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.839 (Fisher's exact test), Q value = 1

Table S23.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients 0 2
ALL 9 3
subtype1 3 1
subtype2 2 1
subtype3 3 0
subtype4 1 1
subtype5 0 0

Figure S21.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.462 (Fisher's exact test), Q value = 0.74

Table S24.  Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 3 19
subtype1 1 4
subtype2 0 5
subtype3 0 3
subtype4 2 3
subtype5 0 4

Figure S22.  Get High-res Image Clustering Approach #2: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #3: 'MIRseq Mature CNMF subtypes'

Table S25.  Description of clustering approach #3: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 7 7 8
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.259 (logrank test), Q value = 0.46

Table S26.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 3 2.7 - 40.1 (21.9)
subtype1 7 0 9.9 - 35.9 (23.2)
subtype2 7 2 2.7 - 40.1 (20.1)
subtype3 8 1 10.2 - 35.0 (27.7)

Figure S23.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.355 (Kruskal-Wallis (anova)), Q value = 0.59

Table S27.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 22 63.9 (9.5)
subtype1 7 66.4 (7.1)
subtype2 7 65.0 (11.5)
subtype3 8 60.6 (9.7)

Figure S24.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S28.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients BLADDER BREAST COLON ENDOMETRIAL KIDNEY LUNG
ALL 3 5 4 3 4 3
subtype1 0 0 0 0 4 3
subtype2 3 0 4 0 0 0
subtype3 0 5 0 3 0 0

Figure S25.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.199 (Fisher's exact test), Q value = 0.4

Table S29.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIC STAGE IV
ALL 5 1 1 1 5 2 1 1 1 1
subtype1 3 1 1 1 0 1 0 0 0 0
subtype2 2 0 0 0 2 0 1 0 1 1
subtype3 0 0 0 0 3 1 0 1 0 0

Figure S26.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0356 (Fisher's exact test), Q value = 0.11

Table S30.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 5 7 7
subtype1 4 2 1
subtype2 1 1 5
subtype3 0 4 1

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.595 (Fisher's exact test), Q value = 0.89

Table S31.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 10 4
subtype1 3 0
subtype2 4 2
subtype3 3 2

Figure S28.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.00397 (Fisher's exact test), Q value = 0.017

Table S32.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 12 10
subtype1 2 5
subtype2 2 5
subtype3 8 0

Figure S29.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0227 (Fisher's exact test), Q value = 0.073

Table S33.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 13 5
subtype1 5 0
subtype2 5 0
subtype3 3 5

Figure S30.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S34.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA KIDNEY CLEAR CELL RENAL CARCINOMA LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG MUCINOUS ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID MUSCLE INVASIVE UROTHELIAL CARCINOMA (PT2 OR ABOVE) SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 2 2 4 1 4 2 1 1 2 2
subtype1 0 0 0 0 4 2 1 0 0 0
subtype2 2 2 0 0 0 0 0 0 2 0
subtype3 0 0 4 1 0 0 0 1 0 2

Figure S31.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

Table S35.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients 0 2
ALL 9 3
subtype1 0 0
subtype2 5 2
subtype3 4 1

Figure S32.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.748 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 3 19
subtype1 0 7
subtype2 1 6
subtype3 2 6

Figure S33.  Get High-res Image Clustering Approach #3: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'MIRseq Mature cHierClus subtypes'

Table S37.  Description of clustering approach #4: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 3 4 3 5 4 3
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0407 (logrank test), Q value = 0.11

Table S38.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 3 2.7 - 40.1 (21.9)
subtype1 3 0 9.9 - 27.8 (23.2)
subtype2 4 0 19.7 - 32.9 (20.4)
subtype3 3 0 20.6 - 33.1 (31.4)
subtype4 5 1 10.2 - 35.0 (24.1)
subtype5 4 0 10.4 - 35.9 (22.2)
subtype6 3 2 2.7 - 40.1 (7.3)

Figure S34.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.783 (Kruskal-Wallis (anova)), Q value = 1

Table S39.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 22 63.9 (9.5)
subtype1 3 67.7 (4.0)
subtype2 4 65.0 (14.9)
subtype3 3 58.7 (11.2)
subtype4 5 61.8 (9.8)
subtype5 4 65.5 (9.3)
subtype6 3 65.0 (8.0)

Figure S35.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S40.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients BLADDER BREAST COLON ENDOMETRIAL KIDNEY LUNG
ALL 3 5 4 3 4 3
subtype1 0 0 0 0 0 3
subtype2 0 0 4 0 0 0
subtype3 0 3 0 0 0 0
subtype4 0 2 0 3 0 0
subtype5 0 0 0 0 4 0
subtype6 3 0 0 0 0 0

Figure S36.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0666 (Fisher's exact test), Q value = 0.18

Table S41.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIC STAGE IV
ALL 5 1 1 1 5 2 1 1 1 1
subtype1 0 1 1 0 0 1 0 0 0 0
subtype2 1 0 0 0 2 0 0 0 1 0
subtype3 0 0 0 0 2 0 0 1 0 0
subtype4 0 0 0 0 1 1 0 0 0 0
subtype5 3 0 0 1 0 0 0 0 0 0
subtype6 1 0 0 0 0 0 1 0 0 1

Figure S37.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.242 (Fisher's exact test), Q value = 0.45

Table S42.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 5 7 7
subtype1 1 1 1
subtype2 0 1 3
subtype3 0 2 1
subtype4 0 2 0
subtype5 3 1 0
subtype6 1 0 2

Figure S38.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

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

Table S43.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 10 4
subtype1 2 0
subtype2 3 1
subtype3 2 1
subtype4 1 1
subtype5 1 0
subtype6 1 1

Figure S39.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00681 (Fisher's exact test), Q value = 0.027

Table S44.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 12 10
subtype1 2 1
subtype2 1 3
subtype3 3 0
subtype4 5 0
subtype5 0 4
subtype6 1 2

Figure S40.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.133 (Fisher's exact test), Q value = 0.3

Table S45.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 13 5
subtype1 3 0
subtype2 4 0
subtype3 1 2
subtype4 2 3
subtype5 2 0
subtype6 1 0

Figure S41.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S46.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA KIDNEY CLEAR CELL RENAL CARCINOMA LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG MUCINOUS ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID MUSCLE INVASIVE UROTHELIAL CARCINOMA (PT2 OR ABOVE) SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 2 2 4 1 4 2 1 1 2 2
subtype1 0 0 0 0 0 2 1 0 0 0
subtype2 2 2 0 0 0 0 0 0 0 0
subtype3 0 0 2 1 0 0 0 0 0 0
subtype4 0 0 2 0 0 0 0 1 0 2
subtype5 0 0 0 0 4 0 0 0 0 0
subtype6 0 0 0 0 0 0 0 0 2 0

Figure S42.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.836 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients 0 2
ALL 9 3
subtype1 0 0
subtype2 3 1
subtype3 3 0
subtype4 1 1
subtype5 0 0
subtype6 2 1

Figure S43.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.534 (Fisher's exact test), Q value = 0.83

Table S48.  Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 3 19
subtype1 0 3
subtype2 1 3
subtype3 0 3
subtype4 2 3
subtype5 0 4
subtype6 0 3

Figure S44.  Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/FPPP-TP/19785501/FPPP-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/FPPP-TP/19775154/FPPP-TP.merged_data.txt

  • Number of patients = 22

  • Number of clustering approaches = 4

  • Number of selected clinical features = 12

  • 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

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.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] 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)
[3] 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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