Correlation between aggregated molecular cancer subtypes and selected clinical features
Sarcoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RX9BJX
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 9 clinical features across 261 patients, 57 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

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

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death',  'TUMOR_TISSUE_SITE',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'GENDER',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'GENDER',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'GENDER',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

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

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

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'GENDER',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 57 significant findings detected.

Clinical
Features
Time
to
Death
YEARS
TO
BIRTH
TUMOR
TISSUE
SITE
GENDER RADIATION
THERAPY
HISTOLOGICAL
TYPE
RESIDUAL
TUMOR
RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.127
(0.181)
5.2e-07
(3.96e-05)
3e-05
(0.000129)
0.00556
(0.0114)
0.325
(0.38)
1e-05
(4.74e-05)
0.177
(0.23)
0.263
(0.32)
0.541
(0.567)
METHLYATION CNMF 0.00201
(0.00475)
8.8e-07
(3.96e-05)
1e-05
(4.74e-05)
0.00918
(0.0172)
0.0007
(0.00202)
1e-05
(4.74e-05)
0.131
(0.185)
0.874
(0.883)
0.433
(0.47)
RPPA CNMF subtypes 3.8e-05
(0.000155)
0.149
(0.201)
1e-05
(4.74e-05)
0.102
(0.15)
0.00041
(0.00123)
1e-05
(4.74e-05)
0.0206
(0.0343)
0.896
(0.896)
0.272
(0.322)
RPPA cHierClus subtypes 0.00174
(0.00423)
0.000968
(0.00262)
1e-05
(4.74e-05)
0.0115
(0.0204)
0.00032
(0.00103)
1e-05
(4.74e-05)
0.00252
(0.00582)
0.369
(0.421)
0.264
(0.32)
RNAseq CNMF subtypes 0.0135
(0.0233)
0.000272
(0.000942)
1e-05
(4.74e-05)
0.00072
(0.00202)
0.00018
(0.000675)
1e-05
(4.74e-05)
3e-05
(0.000129)
0.703
(0.719)
0.375
(0.422)
RNAseq cHierClus subtypes 0.0597
(0.0927)
6.25e-05
(0.000245)
1e-05
(4.74e-05)
0.00684
(0.0134)
0.00985
(0.0181)
1e-05
(4.74e-05)
0.00037
(0.00115)
0.113
(0.163)
0.487
(0.515)
MIRSEQ CNMF 0.563
(0.582)
0.141
(0.195)
0.00032
(0.00103)
0.00763
(0.0146)
0.004
(0.00857)
1e-05
(4.74e-05)
0.0465
(0.0747)
0.368
(0.421)
0.185
(0.238)
MIRSEQ CHIERARCHICAL 0.442
(0.474)
0.00357
(0.00784)
0.0002
(0.00072)
0.0116
(0.0204)
0.198
(0.251)
1e-05
(4.74e-05)
0.00134
(0.00345)
0.242
(0.303)
0.0167
(0.0284)
MIRseq Mature CNMF subtypes 0.00468
(0.0098)
0.0478
(0.0755)
1e-05
(4.74e-05)
0.0909
(0.136)
0.0259
(0.0423)
1e-05
(4.74e-05)
0.0799
(0.122)
0.422
(0.463)
0.267
(0.32)
MIRseq Mature cHierClus subtypes 0.176
(0.23)
0.00612
(0.0122)
1e-05
(4.74e-05)
0.0017
(0.00423)
0.00099
(0.00262)
1e-05
(4.74e-05)
0.00292
(0.00657)
0.145
(0.198)
0.417
(0.463)
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
Number of samples 95 60 39 63
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.127 (logrank test), Q value = 0.18

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

nPatients nDeath Duration Range (Median), Month
ALL 257 98 0.5 - 188.2 (31.1)
subtype1 95 41 0.6 - 150.3 (31.5)
subtype2 60 21 0.7 - 171.1 (34.7)
subtype3 39 9 0.5 - 188.2 (37.0)
subtype4 63 27 1.1 - 123.8 (27.9)

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 'YEARS_TO_BIRTH'

P value = 5.2e-07 (Kruskal-Wallis (anova)), Q value = 4e-05

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

nPatients Mean (Std.Dev)
ALL 256 60.8 (14.7)
subtype1 95 66.3 (12.8)
subtype2 60 52.8 (16.0)
subtype3 39 58.1 (12.4)
subtype4 62 62.0 (14.0)

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00013

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

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 28 2 1 2 1 2 10 2 4 2 17 5 44 4 2 5 8 2 1 73 3 2 5 4 1 7 5
subtype1 0 2 0 0 1 0 6 0 0 0 0 0 1 0 0 0 8 4 17 0 0 2 3 1 1 38 0 1 2 0 0 4 3
subtype2 0 2 2 0 0 0 2 1 1 2 1 0 5 2 2 1 1 0 7 0 1 3 2 0 0 18 1 0 2 1 1 0 2
subtype3 1 1 0 1 0 1 7 1 0 0 0 1 1 0 1 0 1 0 4 3 0 0 2 1 0 9 2 0 0 1 0 1 0
subtype4 0 2 0 0 1 0 13 0 0 0 0 1 3 0 1 1 7 1 16 1 1 0 1 0 0 8 0 1 1 2 0 2 0

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00556 (Fisher's exact test), Q value = 0.011

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

nPatients FEMALE MALE
ALL 139 118
subtype1 44 51
subtype2 26 34
subtype3 27 12
subtype4 42 21

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.325 (Fisher's exact test), Q value = 0.38

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

nPatients NO YES
ALL 179 72
subtype1 63 30
subtype2 46 13
subtype3 29 8
subtype4 41 21

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 59 2 1 103 9 24 28 2 2 6 21
subtype1 32 0 0 21 3 10 16 0 0 0 13
subtype2 27 2 1 10 2 5 1 2 2 6 2
subtype3 0 0 0 32 3 1 2 0 0 0 1
subtype4 0 0 0 40 1 8 9 0 0 0 5

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.177 (Fisher's exact test), Q value = 0.23

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

nPatients R0 R1 R2 RX
ALL 152 69 9 26
subtype1 49 35 4 7
subtype2 40 14 1 5
subtype3 26 5 2 5
subtype4 37 15 2 9

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.263 (Fisher's exact test), Q value = 0.32

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 224
subtype1 4 4 85
subtype2 2 3 54
subtype3 0 4 33
subtype4 0 7 52

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.541 (Fisher's exact test), Q value = 0.57

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 220
subtype1 2 86
subtype2 2 48
subtype3 1 34
subtype4 0 52

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 55 57 73 76
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00201 (logrank test), Q value = 0.0047

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 261 99 0.5 - 188.2 (31.1)
subtype1 55 12 0.7 - 150.3 (31.5)
subtype2 57 23 0.7 - 188.2 (27.8)
subtype3 73 24 0.5 - 123.8 (37.0)
subtype4 76 40 0.6 - 171.1 (24.6)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 8.8e-07 (Kruskal-Wallis (anova)), Q value = 4e-05

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

nPatients Mean (Std.Dev)
ALL 260 60.9 (14.7)
subtype1 55 63.1 (14.3)
subtype2 57 52.5 (17.1)
subtype3 72 59.1 (11.5)
subtype4 76 67.2 (12.3)

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

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 29 2 1 2 1 2 11 2 4 2 17 5 45 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 0 1 0 0 0 0 0 1 0 2 1 0 2 1 0 1 4 0 12 0 0 2 0 0 0 18 0 1 2 0 0 3 3
subtype2 1 1 2 0 1 1 7 0 1 0 0 0 3 1 2 0 0 1 8 1 1 2 3 0 0 17 0 0 0 2 1 0 1
subtype3 0 0 0 1 1 0 18 0 0 0 0 2 3 0 1 0 3 0 6 3 1 0 4 1 1 23 3 0 1 0 0 1 0
subtype4 0 5 0 0 0 0 4 1 0 0 0 0 3 0 1 1 10 4 19 0 0 1 1 1 0 16 0 1 2 2 0 3 1

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00918 (Fisher's exact test), Q value = 0.017

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 142 119
subtype1 23 32
subtype2 35 22
subtype3 49 24
subtype4 35 41

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 7e-04 (Fisher's exact test), Q value = 0.002

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 181 74
subtype1 35 18
subtype2 44 13
subtype3 59 10
subtype4 43 33

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 59 2 1 105 9 25 29 2 2 6 21
subtype1 23 0 1 4 4 5 9 0 0 0 9
subtype2 22 2 0 11 4 5 2 2 2 6 1
subtype3 0 0 0 73 0 0 0 0 0 0 0
subtype4 14 0 0 17 1 15 18 0 0 0 11

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.131 (Fisher's exact test), Q value = 0.18

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 155 70 9 26
subtype1 27 20 1 6
subtype2 33 13 3 8
subtype3 54 13 1 5
subtype4 41 24 4 7

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.874 (Fisher's exact test), Q value = 0.88

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 228
subtype1 2 2 50
subtype2 1 4 51
subtype3 1 7 61
subtype4 2 5 66

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.433 (Fisher's exact test), Q value = 0.47

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 223
subtype1 1 50
subtype2 2 44
subtype3 0 62
subtype4 2 67

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S21.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 60 45 61 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 3.8e-05 (logrank test), Q value = 0.00016

Table S22.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 223 84 0.5 - 188.2 (30.4)
subtype1 60 17 8.6 - 136.4 (32.7)
subtype2 45 27 1.1 - 112.0 (19.7)
subtype3 61 17 0.5 - 188.2 (35.9)
subtype4 57 23 0.6 - 171.1 (25.4)

Figure S19.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.149 (Kruskal-Wallis (anova)), Q value = 0.2

Table S23.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 222 61.9 (14.3)
subtype1 60 60.7 (15.3)
subtype2 45 63.6 (16.0)
subtype3 60 59.6 (11.6)
subtype4 57 64.2 (14.2)

Figure S20.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S24.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 6 1 2 1 22 2 1 1 1 11 2 4 2 15 5 41 3 2 7 2 1 65 2 2 5 4 7 4
subtype1 0 2 0 0 0 1 1 0 0 0 4 1 0 1 2 1 15 0 1 0 0 0 25 0 0 2 1 0 3
subtype2 1 2 1 1 0 5 0 0 0 0 2 0 2 0 4 3 13 0 1 2 1 0 5 0 0 0 1 1 0
subtype3 0 0 0 1 1 14 1 1 1 1 3 0 0 0 2 0 3 3 0 5 1 1 18 2 0 1 1 1 0
subtype4 0 2 0 0 0 2 0 0 0 0 2 1 2 1 7 1 10 0 0 0 0 0 17 0 2 2 1 5 1

Figure S21.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.102 (Fisher's exact test), Q value = 0.15

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

nPatients FEMALE MALE
ALL 117 106
subtype1 27 33
subtype2 23 22
subtype3 40 21
subtype4 27 30

Figure S22.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00041 (Fisher's exact test), Q value = 0.0012

Table S26.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 156 62
subtype1 43 17
subtype2 23 21
subtype3 52 6
subtype4 38 18

Figure S23.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 54 1 82 9 23 29 1 1 4 19
subtype1 30 0 3 2 15 4 1 0 0 5
subtype2 7 0 16 2 2 10 0 1 3 4
subtype3 1 0 57 2 0 1 0 0 0 0
subtype4 16 1 6 3 6 14 0 0 1 10

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0206 (Fisher's exact test), Q value = 0.034

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 128 65 7 22
subtype1 35 22 1 2
subtype2 20 14 3 8
subtype3 41 10 1 9
subtype4 32 19 2 3

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.896 (Fisher's exact test), Q value = 0.9

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 13 196
subtype1 1 2 57
subtype2 1 4 39
subtype3 2 4 53
subtype4 2 3 47

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.272 (Fisher's exact test), Q value = 0.32

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 191
subtype1 2 55
subtype2 1 35
subtype3 0 52
subtype4 0 49

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S31.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 25 36 50 68 22 22
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00174 (logrank test), Q value = 0.0042

Table S32.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 223 84 0.5 - 188.2 (30.4)
subtype1 25 7 4.6 - 136.4 (32.7)
subtype2 36 11 6.3 - 150.3 (36.6)
subtype3 50 23 0.6 - 188.2 (19.6)
subtype4 68 21 0.5 - 159.3 (36.1)
subtype5 22 10 1.1 - 86.8 (33.9)
subtype6 22 12 0.7 - 71.3 (21.2)

Figure S28.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000968 (Kruskal-Wallis (anova)), Q value = 0.0026

Table S33.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 222 61.9 (14.3)
subtype1 25 62.4 (14.9)
subtype2 36 63.6 (12.6)
subtype3 50 68.3 (13.8)
subtype4 67 58.0 (11.6)
subtype5 22 62.8 (14.8)
subtype6 22 54.7 (18.8)

Figure S29.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S34.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 6 1 2 1 22 2 1 1 1 11 2 4 2 15 5 41 3 2 7 2 1 65 2 2 5 4 7 4
subtype1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 9 0 0 1 0 0 9 0 0 2 1 0 1
subtype2 0 1 0 0 0 0 0 0 0 0 4 1 0 1 3 1 6 0 1 0 0 0 15 0 0 0 0 2 1
subtype3 0 3 0 0 0 3 0 0 0 0 0 1 0 1 9 4 14 0 0 0 0 0 6 0 2 1 3 2 1
subtype4 0 0 0 1 1 16 1 0 1 1 3 0 1 0 2 0 5 3 0 5 1 1 21 2 0 1 0 1 1
subtype5 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 2 0 1 0 0 0 14 0 0 0 0 1 0
subtype6 0 1 1 1 0 2 1 1 0 0 3 0 2 0 0 0 5 0 0 1 1 0 0 0 0 1 0 1 0

Figure S30.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0115 (Fisher's exact test), Q value = 0.02

Table S35.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 117 106
subtype1 10 15
subtype2 12 24
subtype3 24 26
subtype4 46 22
subtype5 11 11
subtype6 14 8

Figure S31.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00032 (Fisher's exact test), Q value = 0.001

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 156 62
subtype1 16 9
subtype2 27 9
subtype3 27 22
subtype4 58 7
subtype5 17 5
subtype6 11 10

Figure S32.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S37.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 54 1 82 9 23 29 1 1 4 19
subtype1 10 0 1 0 10 1 0 0 0 3
subtype2 23 0 0 1 5 5 0 0 0 2
subtype3 4 0 12 2 6 15 0 0 0 11
subtype4 3 0 62 0 0 1 1 0 0 1
subtype5 12 0 2 0 0 5 0 0 1 2
subtype6 2 1 5 6 2 2 0 1 3 0

Figure S33.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00252 (Fisher's exact test), Q value = 0.0058

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 128 65 7 22
subtype1 15 9 0 1
subtype2 18 14 1 2
subtype3 27 16 1 6
subtype4 49 12 1 6
subtype5 9 11 2 0
subtype6 10 3 2 7

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.369 (Fisher's exact test), Q value = 0.42

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 13 196
subtype1 0 1 24
subtype2 0 0 36
subtype3 2 5 39
subtype4 3 5 57
subtype5 1 0 21
subtype6 0 2 19

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.264 (Fisher's exact test), Q value = 0.32

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 191
subtype1 1 23
subtype2 0 33
subtype3 2 41
subtype4 0 59
subtype5 0 19
subtype6 0 16

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S41.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 116 78 65
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0135 (logrank test), Q value = 0.023

Table S42.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 259 98 0.5 - 188.2 (31.1)
subtype1 116 40 0.6 - 171.1 (31.0)
subtype2 78 27 0.5 - 159.3 (36.7)
subtype3 65 31 0.7 - 188.2 (21.7)

Figure S37.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000272 (Kruskal-Wallis (anova)), Q value = 0.00094

Table S43.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 60.7 (14.6)
subtype1 116 64.9 (13.4)
subtype2 77 57.9 (11.5)
subtype3 65 56.5 (17.8)

Figure S38.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S44.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 29 2 1 2 1 2 11 2 4 2 17 5 45 4 2 5 8 2 1 72 3 2 5 4 1 7 5
subtype1 0 4 0 0 0 0 1 2 0 1 0 0 5 1 0 2 13 4 31 0 0 2 0 0 0 35 0 2 3 2 0 4 4
subtype2 0 0 0 1 1 0 22 0 0 0 0 2 3 0 1 0 3 0 6 4 1 0 5 1 1 22 3 0 1 0 0 1 0
subtype3 1 3 2 0 1 1 6 0 1 1 1 0 3 1 3 0 1 1 8 0 1 3 3 1 0 15 0 0 1 2 1 2 1

Figure S39.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00072 (Fisher's exact test), Q value = 0.002

Table S45.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 141 118
subtype1 50 66
subtype2 55 23
subtype3 36 29

Figure S40.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00018 (Fisher's exact test), Q value = 0.00067

Table S46.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 179 74
subtype1 67 47
subtype2 64 10
subtype3 48 17

Figure S41.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S47.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 58 2 1 104 9 25 29 2 2 6 21
subtype1 38 0 1 12 1 23 25 0 0 0 16
subtype2 1 0 0 77 0 0 0 0 0 0 0
subtype3 19 2 0 15 8 2 4 2 2 6 5

Figure S42.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00013

Table S48.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 154 69 9 26
subtype1 69 40 3 4
subtype2 57 13 1 7
subtype3 28 16 5 15

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.703 (Fisher's exact test), Q value = 0.72

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 226
subtype1 3 6 103
subtype2 2 8 64
subtype3 1 4 59

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.375 (Fisher's exact test), Q value = 0.42

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 222
subtype1 3 104
subtype2 0 66
subtype3 2 52

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S51.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 48 21 50 47 31 29 33
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0597 (logrank test), Q value = 0.093

Table S52.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 259 98 0.5 - 188.2 (31.1)
subtype1 48 18 3.2 - 150.3 (34.5)
subtype2 21 4 5.3 - 102.0 (29.0)
subtype3 50 17 0.5 - 123.0 (38.4)
subtype4 47 21 0.7 - 188.2 (27.0)
subtype5 31 14 0.6 - 112.0 (18.1)
subtype6 29 9 8.6 - 159.3 (26.9)
subtype7 33 15 1.1 - 171.1 (19.7)

Figure S46.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 6.25e-05 (Kruskal-Wallis (anova)), Q value = 0.00024

Table S53.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 60.7 (14.6)
subtype1 48 66.8 (12.3)
subtype2 21 61.5 (11.7)
subtype3 50 60.0 (12.0)
subtype4 47 54.5 (18.3)
subtype5 31 67.8 (14.0)
subtype6 28 54.1 (9.9)
subtype7 33 60.2 (15.4)

Figure S47.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S54.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 29 2 1 2 1 2 11 2 4 2 17 5 45 4 2 5 8 2 1 72 3 2 5 4 1 7 5
subtype1 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 1 11 0 0 1 0 0 0 23 0 0 1 1 0 3 1
subtype2 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 3 0 3 0 0 1 0 0 0 6 0 1 2 0 0 0 1
subtype3 0 0 0 1 1 0 1 0 0 0 0 2 2 0 0 0 3 0 6 2 1 0 4 1 1 21 2 0 1 0 0 1 0
subtype4 0 1 2 0 1 0 4 0 1 1 0 0 3 0 2 0 0 0 7 0 1 2 3 1 0 12 0 0 1 2 1 1 0
subtype5 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 7 2 10 0 0 0 0 0 0 3 0 1 0 0 0 2 2
subtype6 0 0 0 0 0 1 20 0 0 0 1 0 1 0 1 0 0 0 0 2 0 0 1 0 0 1 1 0 0 0 0 0 0
subtype7 1 2 0 0 0 0 2 1 0 0 0 0 3 1 1 1 2 2 8 0 0 1 0 0 0 6 0 0 0 1 0 0 1

Figure S48.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00684 (Fisher's exact test), Q value = 0.013

Table S55.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 141 118
subtype1 21 27
subtype2 10 11
subtype3 30 20
subtype4 25 22
subtype5 15 16
subtype6 25 4
subtype7 15 18

Figure S49.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00985 (Fisher's exact test), Q value = 0.018

Table S56.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 179 74
subtype1 32 15
subtype2 13 8
subtype3 40 8
subtype4 32 15
subtype5 18 13
subtype6 25 2
subtype7 19 13

Figure S50.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S57.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 58 2 1 104 9 25 29 2 2 6 21
subtype1 22 0 0 0 0 14 7 0 0 0 5
subtype2 9 0 0 1 0 3 5 0 0 0 3
subtype3 0 0 0 50 0 0 0 0 0 0 0
subtype4 16 0 0 7 8 2 2 1 2 6 3
subtype5 3 0 0 9 0 5 9 1 0 0 4
subtype6 1 0 0 28 0 0 0 0 0 0 0
subtype7 7 2 1 9 1 1 6 0 0 0 6

Figure S51.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00037 (Fisher's exact test), Q value = 0.0011

Table S58.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 154 69 9 26
subtype1 26 21 1 0
subtype2 12 6 0 3
subtype3 38 11 0 1
subtype4 21 11 5 9
subtype5 19 8 1 3
subtype6 20 2 1 6
subtype7 18 10 1 4

Figure S52.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.113 (Fisher's exact test), Q value = 0.16

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 226
subtype1 1 1 46
subtype2 1 2 18
subtype3 0 5 44
subtype4 1 2 43
subtype5 1 0 26
subtype6 2 3 21
subtype7 0 5 28

Figure S53.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.487 (Fisher's exact test), Q value = 0.52

Table S60.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 222
subtype1 1 45
subtype2 1 19
subtype3 0 46
subtype4 2 37
subtype5 1 25
subtype6 0 21
subtype7 0 29

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

Clustering Approach #7: 'MIRSEQ CNMF'

Table S61.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 107 51 101
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.563 (logrank test), Q value = 0.58

Table S62.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 259 98 0.5 - 188.2 (31.1)
subtype1 107 43 0.6 - 188.2 (29.5)
subtype2 51 18 3.1 - 150.3 (32.0)
subtype3 101 37 0.5 - 159.3 (31.1)

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.19

Table S63.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 60.9 (14.7)
subtype1 107 60.3 (17.1)
subtype2 51 64.5 (13.3)
subtype3 100 59.6 (12.4)

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.00032 (Fisher's exact test), Q value = 0.001

Table S64.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 28 2 1 2 1 2 11 2 4 2 16 5 45 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 1 3 2 0 1 0 6 1 1 0 0 0 7 1 2 1 5 2 15 0 1 4 3 1 0 36 0 1 2 3 1 2 4
subtype2 0 3 0 0 0 0 1 0 0 0 0 0 0 0 1 1 7 2 17 0 0 1 0 0 0 11 0 1 2 1 0 2 1
subtype3 0 1 0 1 1 1 21 1 0 2 1 2 4 1 1 0 4 1 13 4 1 0 5 1 1 27 3 0 1 0 0 3 0

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00763 (Fisher's exact test), Q value = 0.015

Table S65.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 140 119
subtype1 47 60
subtype2 27 24
subtype3 66 35

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.004 (Fisher's exact test), Q value = 0.0086

Table S66.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 180 73
subtype1 79 28
subtype2 26 24
subtype3 75 21

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S67.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 59 2 1 104 9 25 29 2 2 6 20
subtype1 41 2 0 16 5 9 14 2 2 6 10
subtype2 13 0 0 5 3 11 11 0 0 0 8
subtype3 5 0 1 83 1 5 4 0 0 0 2

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0465 (Fisher's exact test), Q value = 0.075

Table S68.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 154 69 9 26
subtype1 55 36 6 10
subtype2 28 15 0 7
subtype3 71 18 3 9

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.368 (Fisher's exact test), Q value = 0.42

Table S69.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 227
subtype1 1 5 100
subtype2 1 4 44
subtype3 4 9 83

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.185 (Fisher's exact test), Q value = 0.24

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 222
subtype1 3 92
subtype2 2 45
subtype3 0 85

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S71.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 84 38 115 22
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.442 (logrank test), Q value = 0.47

Table S72.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 259 98 0.5 - 188.2 (31.1)
subtype1 84 34 0.6 - 171.1 (29.2)
subtype2 38 12 0.7 - 150.3 (32.4)
subtype3 115 42 0.5 - 159.3 (33.3)
subtype4 22 10 1.1 - 188.2 (29.5)

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00357 (Kruskal-Wallis (anova)), Q value = 0.0078

Table S73.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 60.9 (14.7)
subtype1 84 63.5 (14.4)
subtype2 38 64.2 (14.2)
subtype3 114 60.2 (12.7)
subtype4 22 48.6 (20.1)

Figure S65.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 2e-04 (Fisher's exact test), Q value = 0.00072

Table S74.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK HEAD AND NECK - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - PANCREAS RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK SUPERFICIAL TRUNK - FLANK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 2 1 2 1 28 2 1 2 1 2 11 2 4 2 16 5 45 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 0 1 0 0 1 0 5 1 0 0 0 0 4 1 1 1 3 2 15 0 1 4 1 1 0 33 0 1 2 1 0 2 3
subtype2 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 6 2 10 0 0 0 0 0 0 9 0 0 2 0 0 2 2
subtype3 0 4 0 1 1 0 22 1 0 1 1 2 4 1 1 0 7 1 17 4 1 0 5 1 1 29 3 1 1 2 0 3 0
subtype4 1 1 2 0 0 0 1 0 1 0 0 0 3 0 2 0 0 0 3 0 0 1 2 0 0 3 0 0 0 1 1 0 0

Figure S66.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0116 (Fisher's exact test), Q value = 0.02

Table S75.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 140 119
subtype1 35 49
subtype2 18 20
subtype3 72 43
subtype4 15 7

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.198 (Fisher's exact test), Q value = 0.25

Table S76.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 180 73
subtype1 60 24
subtype2 22 16
subtype3 83 26
subtype4 15 7

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S77.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR GIANT CELL 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA WITH GIANT CELLS LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 59 2 1 104 9 25 29 2 2 6 20
subtype1 37 2 0 12 1 10 15 0 0 0 7
subtype2 11 0 0 4 5 7 6 1 0 0 4
subtype3 7 0 1 84 0 8 7 0 0 0 8
subtype4 4 0 0 4 3 0 1 1 2 6 1

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00134 (Fisher's exact test), Q value = 0.0034

Table S78.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 154 69 9 26
subtype1 41 35 3 5
subtype2 23 6 0 8
subtype3 77 25 3 10
subtype4 13 3 3 3

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.242 (Fisher's exact test), Q value = 0.3

Table S79.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 227
subtype1 1 2 79
subtype2 1 2 34
subtype3 4 12 94
subtype4 0 2 20

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0167 (Fisher's exact test), Q value = 0.028

Table S80.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 222
subtype1 2 75
subtype2 1 36
subtype3 0 97
subtype4 2 14

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S81.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 60 27 48 23 40
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00468 (logrank test), Q value = 0.0098

Table S82.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 198 76 0.5 - 188.2 (27.9)
subtype1 60 23 0.6 - 188.2 (23.0)
subtype2 27 7 3.2 - 150.3 (33.4)
subtype3 48 17 0.5 - 123.0 (36.1)
subtype4 23 15 0.7 - 71.3 (22.9)
subtype5 40 14 3.1 - 171.1 (24.4)

Figure S73.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0478 (Kruskal-Wallis (anova)), Q value = 0.076

Table S83.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 197 61.2 (14.9)
subtype1 60 57.7 (18.1)
subtype2 27 65.9 (11.6)
subtype3 47 60.2 (10.9)
subtype4 23 57.7 (15.7)
subtype5 40 66.8 (13.2)

Figure S74.  Get High-res Image Clustering Approach #9: '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 = 4.7e-05

Table S84.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 1 1 2 1 21 1 1 1 2 8 2 4 1 13 4 42 2 2 3 5 1 52 2 2 4 2 5 4
subtype1 1 2 1 0 1 0 2 0 1 0 0 3 1 2 0 2 1 9 0 1 3 2 1 22 0 0 1 1 1 2
subtype2 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 8 0 0 0 0 0 14 0 0 0 1 0 0
subtype3 0 0 0 1 1 1 11 0 0 1 2 2 0 1 0 2 0 6 1 1 0 2 0 13 2 0 0 0 1 0
subtype4 0 1 0 0 0 0 7 0 0 0 0 2 0 1 0 1 1 6 1 0 0 1 0 2 0 0 0 0 0 0
subtype5 0 2 0 0 0 0 1 1 0 0 0 0 1 0 1 7 2 13 0 0 0 0 0 1 0 2 3 0 3 2

Figure S75.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0909 (Fisher's exact test), Q value = 0.14

Table S85.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 111 87
subtype1 29 31
subtype2 12 15
subtype3 33 15
subtype4 16 7
subtype5 21 19

Figure S76.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0259 (Fisher's exact test), Q value = 0.042

Table S86.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 133 62
subtype1 45 15
subtype2 18 8
subtype3 36 10
subtype4 15 8
subtype5 19 21

Figure S77.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S87.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 43 2 72 8 21 28 2 1 5 16
subtype1 23 2 7 4 6 7 2 1 3 5
subtype2 15 0 0 0 5 5 0 0 0 2
subtype3 1 0 46 0 1 0 0 0 0 0
subtype4 2 0 13 1 2 2 0 0 2 1
subtype5 2 0 6 3 7 14 0 0 0 8

Figure S78.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0799 (Fisher's exact test), Q value = 0.12

Table S88.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 115 56 8 18
subtype1 30 22 2 6
subtype2 12 10 3 2
subtype3 34 10 0 4
subtype4 12 5 3 3
subtype5 27 9 0 3

Figure S79.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.422 (Fisher's exact test), Q value = 0.46

Table S89.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 13 173
subtype1 1 1 57
subtype2 0 2 25
subtype3 1 5 39
subtype4 0 2 20
subtype5 2 3 32

Figure S80.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.267 (Fisher's exact test), Q value = 0.32

Table S90.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 172
subtype1 1 51
subtype2 0 27
subtype3 0 43
subtype4 1 18
subtype5 2 33

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S91.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 59 50 21 43 25
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.176 (logrank test), Q value = 0.23

Table S92.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 198 76 0.5 - 188.2 (27.9)
subtype1 59 21 0.6 - 171.1 (34.7)
subtype2 50 18 0.5 - 123.0 (34.3)
subtype3 21 9 0.7 - 102.0 (22.9)
subtype4 43 16 3.1 - 150.3 (25.4)
subtype5 25 12 1.1 - 188.2 (19.6)

Figure S82.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00612 (Kruskal-Wallis (anova)), Q value = 0.012

Table S93.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 197 61.2 (14.9)
subtype1 59 63.5 (13.9)
subtype2 49 59.5 (11.7)
subtype3 21 65.4 (13.1)
subtype4 43 64.9 (13.7)
subtype5 25 49.5 (19.8)

Figure S83.  Get High-res Image Clustering Approach #10: '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 = 4.7e-05

Table S94.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients CHEST - BREAST CHEST - CHEST WALL CHEST - LUNG/PLEURA CHEST - MEDIASTINUM CHEST - OTHER (PLEASE SPECIFY GYNECOLOGICAL - OVARY GYNECOLOGICAL - UTERUS HEAD AND NECK - HEAD HEAD AND NECK - NECK LOWER ABDOMINAL/PELVIC - BLADDER LOWER ABDOMINAL/PELVIC - OTHER (PLEASE SPECIFY LOWER ABDOMINAL/PELVIC - PELVIC LOWER ABDOMINAL/PELVIC - SPERMATIC CORD LOWER EXTREMITY - FOOT/ANKLE LOWER EXTREMITY - GROIN LOWER EXTREMITY - LOWER LEG/CALF LOWER EXTREMITY - OTHER (PLEASE SPECIFY LOWER EXTREMITY - THIGH/KNEE RETROPERITONEUM/UPPER ABDOMINAL - COLON RETROPERITONEUM/UPPER ABDOMINAL - GASTRIC RETROPERITONEUM/UPPER ABDOMINAL - INTRAABDOMINAL RETROPERITONEUM/UPPER ABDOMINAL - KIDNEY RETROPERITONEUM/UPPER ABDOMINAL - OTHER (PLEASE SPECIFY RETROPERITONEUM/UPPER ABDOMINAL - RETROPERITONEUM RETROPERITONEUM/UPPER ABDOMINAL - SMALL INTESTINES SUPERFICIAL TRUNK - ABDOMINAL WALL SUPERFICIAL TRUNK - BACK SUPERFICIAL TRUNK - BUTTOCK UPPER EXTREMITY - SHOULDER/AXILLA UPPER EXTREMITY - UPPER ARM/ELBOW
ALL 1 7 1 1 2 1 21 1 1 1 2 8 2 4 1 13 4 42 2 2 3 5 1 52 2 2 4 2 5 4
subtype1 0 2 0 0 0 1 2 0 0 0 0 3 1 0 1 2 1 10 0 1 1 1 1 28 0 0 1 1 1 1
subtype2 0 0 0 1 1 0 14 0 0 1 2 3 0 1 0 2 0 6 1 1 0 2 0 12 2 0 0 0 1 0
subtype3 0 4 0 0 0 0 1 0 0 0 0 0 1 0 0 3 0 8 1 0 0 0 0 3 0 0 0 0 0 0
subtype4 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 6 3 14 0 0 0 0 0 6 0 2 3 0 3 2
subtype5 1 1 1 0 1 0 3 0 1 0 0 2 0 2 0 0 0 4 0 0 2 2 0 3 0 0 0 1 0 1

Figure S84.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0017 (Fisher's exact test), Q value = 0.0042

Table S95.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 111 87
subtype1 23 36
subtype2 36 14
subtype3 12 9
subtype4 21 22
subtype5 19 6

Figure S85.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00099 (Fisher's exact test), Q value = 0.0026

Table S96.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 133 62
subtype1 46 13
subtype2 39 9
subtype3 9 11
subtype4 21 22
subtype5 18 7

Figure S86.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S97.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients DEDIFFERENTIATED LIPOSARCOMA DESMOID TUMOR LEIOMYOSARCOMA (LMS) MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNST) MYXOFIBROSARCOMA PLEOMORPHIC 'MFH' / UNDIFFERENTIATED PLEOMORPHIC SARCOMA SARCOMA; SYNOVIAL; POORLY DIFFERENTIATED SYNOVIAL SARCOMA - BIPHASIC SYNOVIAL SARCOMA - MONOPHASIC UNDIFFERENTIATED PLEOMORPHIC SARCOMA (UPS)
ALL 43 2 72 8 21 28 2 1 5 16
subtype1 27 2 10 0 7 7 0 0 0 6
subtype2 1 0 49 0 0 0 0 0 0 0
subtype3 3 0 3 0 6 5 0 0 0 4
subtype4 6 0 4 4 8 15 0 0 0 6
subtype5 6 0 6 4 0 1 2 1 5 0

Figure S87.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00292 (Fisher's exact test), Q value = 0.0066

Table S98.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 115 56 8 18
subtype1 26 27 2 4
subtype2 37 10 0 3
subtype3 10 8 2 1
subtype4 28 7 1 6
subtype5 14 4 3 4

Figure S88.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.145 (Fisher's exact test), Q value = 0.2

Table S99.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 13 173
subtype1 0 1 57
subtype2 1 6 40
subtype3 1 2 16
subtype4 1 2 38
subtype5 1 2 22

Figure S89.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.417 (Fisher's exact test), Q value = 0.46

Table S100.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 172
subtype1 1 53
subtype2 0 45
subtype3 0 17
subtype4 2 40
subtype5 1 17

Figure S90.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/SARC-TP/22542442/SARC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SARC-TP/22507207/SARC-TP.merged_data.txt

  • Number of patients = 261

  • Number of clustering approaches = 10

  • Number of selected clinical features = 9

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)