Correlation between aggregated molecular cancer subtypes and selected clinical features
Sarcoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1RX9BCP
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RX9BCP
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 260 patients, 56 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',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 7 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 'Time to Death',  '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', and 'HISTOLOGICAL_TYPE'.

  • 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'.

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

  • 8 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, 56 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 0.223
(0.29) 		0.000388
(0.00116) 		9e-05
(0.000352) 		0.0153
(0.0292) 		0.018
(0.0325) 		1e-05
(5.29e-05) 		0.306
(0.353) 		0.518
(0.536) 		1
(1.00)
METHLYATION CNMF 0.00377
(0.00894) 		0.000139
(0.000502) 		1e-05
(5.29e-05) 		0.0001
(0.000375) 		0.0106
(0.0224) 		1e-05
(5.29e-05) 		0.38
(0.422) 		0.17
(0.228) 		0.45
(0.487)
RPPA CNMF subtypes 7.41e-05
(0.000303) 		0.157
(0.218) 		1e-05
(5.29e-05) 		0.12
(0.176) 		0.00152
(0.00391) 		1e-05
(5.29e-05) 		0.0245
(0.0416) 		0.904
(0.914) 		0.272
(0.335)
RPPA cHierClus subtypes 0.00283
(0.00688) 		0.000601
(0.00175) 		1e-05
(5.29e-05) 		0.0259
(0.0431) 		0.00151
(0.00391) 		1e-05
(5.29e-05) 		0.00753
(0.0169) 		0.483
(0.517) 		0.256
(0.324)
RNAseq CNMF subtypes 0.016
(0.03) 		0.000194
(0.000646) 		1e-05
(5.29e-05) 		0.00081
(0.00228) 		0.00017
(0.000588) 		1e-05
(5.29e-05) 		5e-05
(0.000237) 		0.715
(0.731) 		0.38
(0.422)
RNAseq cHierClus subtypes 0.00677
(0.0156) 		6.45e-05
(0.00029) 		1e-05
(5.29e-05) 		0.00793
(0.0174) 		0.0142
(0.0283) 		1e-05
(5.29e-05) 		0.0003
(0.000964) 		0.142
(0.201) 		0.508
(0.532)
MIRSEQ CNMF 0.29
(0.344) 		0.178
(0.235) 		0.00108
(0.00295) 		0.0401
(0.0656) 		0.0465
(0.0748) 		1e-05
(5.29e-05) 		0.0802
(0.124) 		0.494
(0.523) 		0.23
(0.295)
MIRSEQ CHIERARCHICAL 0.299
(0.349) 		0.00035
(0.00109) 		2e-05
(1e-04) 		0.0184
(0.0325) 		0.121
(0.176) 		1e-05
(5.29e-05) 		7e-05
(3e-04) 		0.355
(0.405) 		0.0107
(0.0224)
MIRseq Mature CNMF subtypes 0.443
(0.487) 		0.163
(0.222) 		1e-05
(5.29e-05) 		0.143
(0.201) 		0.0151
(0.0292) 		1e-05
(5.29e-05) 		0.0692
(0.109) 		0.276
(0.335) 		0.285
(0.343)
MIRseq Mature cHierClus subtypes 0.087
(0.133) 		0.0112
(0.023) 		1e-05
(5.29e-05) 		0.00227
(0.00567) 		0.0172
(0.0317) 		1e-05
(5.29e-05) 		0.0187
(0.0325) 		0.0994
(0.149) 		0.273
(0.335)
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 70 86 74 26
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 0.29

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

nPatients nDeath Duration Range (Median), Month
ALL 256 94 0.5 - 188.2 (26.4)
subtype1 70 28 1.1 - 124.4 (23.4)
subtype2 86 27 0.5 - 143.4 (34.7)
subtype3 74 32 0.6 - 188.2 (27.2)
subtype4 26 7 3.9 - 124.7 (23.4)

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 = 0.000388 (Kruskal-Wallis (anova)), Q value = 0.0012

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

nPatients Mean (Std.Dev)
ALL 255 60.9 (14.7)
subtype1 69 61.7 (14.1)
subtype2 86 56.0 (16.2)
subtype3 74 65.8 (11.9)
subtype4 26 61.5 (13.5)

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 = 9e-05 (Fisher's exact test), Q value = 0.00035

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 43 4 2 5 8 2 1 73 3 2 5 4 1 7 5
subtype1 0 2 0 1 1 0 13 0 0 0 0 0 3 0 0 0 7 4 19 1 1 0 1 0 0 8 0 1 1 2 0 4 1
subtype2 1 3 2 0 0 0 6 2 1 2 1 0 4 2 4 2 2 0 10 3 1 3 4 1 0 24 2 0 2 1 1 0 2
subtype3 0 2 0 0 1 1 9 0 0 0 0 2 2 0 0 0 8 1 13 0 0 1 1 0 1 23 1 1 2 1 0 3 0
subtype4 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 2 1 0 18 0 0 0 0 0 0 2

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 = 0.0153 (Fisher's exact test), Q value = 0.029

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

nPatients FEMALE MALE
ALL 139 117
subtype1 46 24
subtype2 43 43
subtype3 42 32
subtype4 8 18

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 = 0.018 (Fisher's exact test), Q value = 0.032

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

nPatients NO YES
ALL 180 69
subtype1 40 28
subtype2 68 15
subtype3 52 20
subtype4 20 6

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 = 5.3e-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 23 28 2 2 6 21
subtype1 2 0 0 37 1 10 13 0 0 0 7
subtype2 21 2 1 37 3 4 4 2 2 6 4
subtype3 14 0 0 28 5 8 11 0 0 0 8
subtype4 22 0 0 1 0 1 0 0 0 0 2

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 = 0.306 (Fisher's exact test), Q value = 0.35

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

nPatients R0 R1 R2 RX
ALL 152 68 9 26
subtype1 40 18 3 9
subtype2 59 19 1 7
subtype3 42 19 4 8
subtype4 11 12 1 2

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 = 0.518 (Fisher's exact test), Q value = 0.54

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 223
subtype1 2 8 55
subtype2 2 6 77
subtype3 2 4 65
subtype4 0 0 26

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

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 1 62
subtype2 2 70
subtype3 2 62
subtype4 0 26

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 5 6 7
Number of samples 49 47 62 35 47 15 5
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00377 (logrank test), Q value = 0.0089

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

nPatients nDeath Duration Range (Median), Month
ALL 260 95 0.5 - 188.2 (26.4)
subtype1 49 17 4.6 - 124.7 (34.5)
subtype2 47 12 0.7 - 102.0 (20.1)
subtype3 62 21 0.5 - 120.2 (35.1)
subtype4 35 19 0.7 - 135.5 (19.7)
subtype5 47 22 0.6 - 143.4 (20.3)
subtype6 15 3 7.5 - 123.8 (27.8)
subtype7 5 1 13.6 - 188.2 (31.6)

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 = 0.000139 (Kruskal-Wallis (anova)), Q value = 5e-04

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

nPatients Mean (Std.Dev)
ALL 259 61.0 (14.6)
subtype1 49 64.2 (12.8)
subtype2 47 63.9 (14.4)
subtype3 61 59.8 (12.0)
subtype4 35 51.2 (19.0)
subtype5 47 65.8 (14.0)
subtype6 15 52.9 (8.6)
subtype7 5 63.6 (6.7)

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 = 5.3e-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 44 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 0 2 0 0 0 0 0 0 0 0 0 0 3 0 0 0 2 1 12 0 0 0 1 0 0 22 0 0 2 2 0 1 1
subtype2 0 1 0 0 0 0 0 1 0 2 1 0 2 1 0 1 3 0 8 0 0 2 0 0 0 16 0 2 2 0 0 2 2
subtype3 0 0 0 1 1 0 9 0 0 0 0 2 3 0 0 0 3 0 6 3 1 0 4 1 1 23 2 0 1 0 0 1 0
subtype4 1 1 2 0 1 1 6 0 1 0 0 0 1 0 3 0 0 0 6 0 0 2 2 0 0 6 0 0 0 0 1 0 1
subtype5 0 3 0 0 0 0 4 1 0 0 0 0 2 1 0 1 8 4 12 0 0 1 0 0 0 5 0 0 0 1 0 3 1
subtype6 0 0 0 0 0 0 7 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 2 1 0 0 0 0 0 0
subtype7 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 1e-04 (Fisher's exact test), Q value = 0.00037

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

nPatients FEMALE MALE
ALL 142 118
subtype1 18 31
subtype2 19 28
subtype3 40 22
subtype4 27 8
subtype5 22 25
subtype6 11 4
subtype7 5 0

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.0106 (Fisher's exact test), Q value = 0.022

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

nPatients NO YES
ALL 182 71
subtype1 32 17
subtype2 32 13
subtype3 49 9
subtype4 26 9
subtype5 25 21
subtype6 14 1
subtype7 4 1

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 = 5.3e-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 24 29 2 2 6 21
subtype1 22 0 0 1 0 13 9 0 0 0 4
subtype2 19 0 1 4 4 3 7 0 0 0 9
subtype3 0 0 0 62 0 0 0 0 0 0 0
subtype4 8 0 0 11 3 2 1 2 2 6 0
subtype5 5 2 0 14 1 5 12 0 0 0 8
subtype6 4 0 0 10 0 1 0 0 0 0 0
subtype7 1 0 0 3 1 0 0 0 0 0 0

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.38 (Fisher's exact test), Q value = 0.42

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

nPatients R0 R1 R2 RX
ALL 155 69 9 26
subtype1 27 18 2 2
subtype2 26 14 1 5
subtype3 44 13 1 4
subtype4 19 7 3 6
subtype5 24 14 2 7
subtype6 11 3 0 1
subtype7 4 0 0 1

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.17 (Fisher's exact test), Q value = 0.23

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 227
subtype1 0 2 47
subtype2 2 2 42
subtype3 0 7 52
subtype4 1 1 32
subtype5 1 4 39
subtype6 2 2 11
subtype7 0 0 4

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.45 (Fisher's exact test), Q value = 0.49

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 46
subtype2 1 43
subtype3 0 55
subtype4 2 25
subtype5 1 40
subtype6 0 11
subtype7 0 3

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 59 45 61 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 7.41e-05 (logrank test), Q value = 3e-04

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

nPatients nDeath Duration Range (Median), Month
ALL 222 81 0.5 - 188.2 (25.6)
subtype1 59 16 8.6 - 124.7 (30.5)
subtype2 45 26 1.1 - 112.0 (19.7)
subtype3 61 16 0.5 - 188.2 (28.2)
subtype4 57 23 0.6 - 143.4 (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 = 0.157 (Kruskal-Wallis (anova)), Q value = 0.22

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

nPatients Mean (Std.Dev)
ALL 221 62.0 (14.2)
subtype1 59 61.2 (15.1)
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 = 5.3e-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 40 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 14 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 = 0.12 (Fisher's exact test), Q value = 0.18

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

nPatients FEMALE MALE
ALL 117 105
subtype1 27 32
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 = 0.00152 (Fisher's exact test), Q value = 0.0039

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

nPatients NO YES
ALL 158 59
subtype1 42 17
subtype2 25 19
subtype3 52 6
subtype4 39 17

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 = 5.3e-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 22 29 1 1 4 19
subtype1 30 0 3 2 14 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 = 0.0245 (Fisher's exact test), Q value = 0.042

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

nPatients R0 R1 R2 RX
ALL 128 64 7 22
subtype1 35 21 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 = 0.904 (Fisher's exact test), Q value = 0.91

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 13 195
subtype1 1 2 56
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 = 0.34

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 27 37 49 68 19 22
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00283 (logrank test), Q value = 0.0069

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

nPatients nDeath Duration Range (Median), Month
ALL 222 81 0.5 - 188.2 (25.6)
subtype1 27 7 4.6 - 124.7 (31.9)
subtype2 37 12 5.3 - 124.4 (33.4)
subtype3 49 22 0.6 - 188.2 (19.4)
subtype4 68 20 0.5 - 123.8 (30.5)
subtype5 19 8 1.1 - 86.8 (35.5)
subtype6 22 12 0.7 - 71.3 (18.7)

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 = 0.000601 (Kruskal-Wallis (anova)), Q value = 0.0017

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

nPatients Mean (Std.Dev)
ALL 221 62.0 (14.2)
subtype1 27 62.4 (14.5)
subtype2 37 63.6 (12.1)
subtype3 49 68.5 (13.8)
subtype4 67 58.0 (11.6)
subtype5 19 63.9 (14.7)
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 = 5.3e-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 40 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 1 0 9 0 0 1 0 0 10 0 0 2 1 0 1
subtype2 0 1 0 0 0 0 0 0 0 0 4 1 0 1 4 1 5 0 1 0 0 0 16 0 0 0 0 2 1
subtype3 0 3 0 0 0 3 0 0 0 0 0 1 0 1 8 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 0 0 2 0 1 0 0 0 12 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 = 0.0259 (Fisher's exact test), Q value = 0.043

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

nPatients FEMALE MALE
ALL 117 105
subtype1 12 15
subtype2 14 23
subtype3 23 26
subtype4 46 22
subtype5 8 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 = 0.00151 (Fisher's exact test), Q value = 0.0039

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

nPatients NO YES
ALL 158 59
subtype1 17 10
subtype2 28 9
subtype3 28 20
subtype4 58 7
subtype5 15 4
subtype6 12 9

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 = 5.3e-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 22 29 1 1 4 19
subtype1 11 0 1 0 10 2 0 0 0 3
subtype2 24 0 0 1 5 5 0 0 0 2
subtype3 4 0 12 2 5 15 0 0 0 11
subtype4 3 0 62 0 0 1 1 0 0 1
subtype5 10 0 2 0 0 4 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 = 0.00753 (Fisher's exact test), Q value = 0.017

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

nPatients R0 R1 R2 RX
ALL 128 64 7 22
subtype1 15 10 1 1
subtype2 18 15 1 2
subtype3 27 15 1 6
subtype4 49 12 1 6
subtype5 9 9 1 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 = 0.483 (Fisher's exact test), Q value = 0.52

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 13 195
subtype1 0 1 26
subtype2 1 0 36
subtype3 1 5 39
subtype4 3 5 57
subtype5 1 0 18
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 = 0.256 (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 25
subtype2 0 35
subtype3 2 40
subtype4 0 59
subtype5 0 16
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 64
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.016 (logrank test), Q value = 0.03

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

nPatients nDeath Duration Range (Median), Month
ALL 258 94 0.5 - 188.2 (26.4)
subtype1 116 39 0.6 - 143.4 (27.1)
subtype2 78 25 0.5 - 123.8 (34.9)
subtype3 64 30 0.7 - 188.2 (19.7)

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 = 0.000194 (Kruskal-Wallis (anova)), Q value = 0.00065

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

nPatients Mean (Std.Dev)
ALL 257 60.8 (14.6)
subtype1 116 65.1 (13.1)
subtype2 77 57.9 (11.5)
subtype3 64 56.5 (18.0)

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 = 5.3e-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 44 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 7 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 = 0.00081 (Fisher's exact test), Q value = 0.0023

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

nPatients FEMALE MALE
ALL 141 117
subtype1 50 66
subtype2 55 23
subtype3 36 28

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 = 0.00017 (Fisher's exact test), Q value = 0.00059

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

nPatients NO YES
ALL 180 71
subtype1 68 46
subtype2 63 10
subtype3 49 15

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 = 5.3e-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 24 29 2 2 6 21
subtype1 38 0 1 12 1 22 26 0 0 0 16
subtype2 1 0 0 77 0 0 0 0 0 0 0
subtype3 19 2 0 15 8 2 3 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 = 5e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients R0 R1 R2 RX
ALL 154 68 9 26
subtype1 69 40 3 4
subtype2 57 13 1 7
subtype3 28 15 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 = 0.715 (Fisher's exact test), Q value = 0.73

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 225
subtype1 3 6 103
subtype2 2 8 64
subtype3 1 4 58

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.38 (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 47 23 50 47 29 29 33
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00677 (logrank test), Q value = 0.016

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

nPatients nDeath Duration Range (Median), Month
ALL 258 94 0.5 - 188.2 (26.4)
subtype1 47 17 3.2 - 124.4 (33.5)
subtype2 23 3 5.3 - 102.0 (33.1)
subtype3 50 16 0.5 - 120.2 (38.4)
subtype4 47 21 0.7 - 188.2 (22.4)
subtype5 29 11 0.6 - 112.0 (17.5)
subtype6 29 9 7.5 - 123.8 (24.6)
subtype7 33 17 1.1 - 143.4 (17.8)

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 = 6.45e-05 (Kruskal-Wallis (anova)), Q value = 0.00029

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

nPatients Mean (Std.Dev)
ALL 257 60.8 (14.6)
subtype1 47 67.0 (12.4)
subtype2 23 61.4 (11.1)
subtype3 50 60.0 (12.0)
subtype4 47 54.5 (18.3)
subtype5 29 67.7 (14.3)
subtype6 28 54.1 (9.9)
subtype7 33 61.5 (15.1)

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 = 5.3e-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 44 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 2 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 1 2
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 2 0 1 0 0 0 1 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 7 0 0 1 0 0 0 7 0 0 0 1 0 1 0

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 = 0.00793 (Fisher's exact test), Q value = 0.017

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

nPatients FEMALE MALE
ALL 141 117
subtype1 21 26
subtype2 10 13
subtype3 30 20
subtype4 25 22
subtype5 14 15
subtype6 25 4
subtype7 16 17

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 = 0.0142 (Fisher's exact test), Q value = 0.028

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

nPatients NO YES
ALL 180 71
subtype1 32 14
subtype2 13 9
subtype3 40 8
subtype4 33 14
subtype5 18 11
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 = 5.3e-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 24 29 2 2 6 21
subtype1 22 0 0 0 0 14 6 0 0 0 5
subtype2 10 0 0 1 0 3 6 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 2 0 0 8 0 5 9 1 0 0 4
subtype6 1 0 0 28 0 0 0 0 0 0 0
subtype7 7 2 1 10 1 0 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 = 3e-04 (Fisher's exact test), Q value = 0.00096

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

nPatients R0 R1 R2 RX
ALL 154 68 9 26
subtype1 25 21 1 0
subtype2 14 6 0 3
subtype3 38 11 0 1
subtype4 21 11 5 9
subtype5 18 8 1 2
subtype6 20 2 1 6
subtype7 18 9 1 5

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 = 0.142 (Fisher's exact test), Q value = 0.2

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 225
subtype1 1 1 45
subtype2 1 2 20
subtype3 0 5 44
subtype4 1 2 43
subtype5 1 0 24
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 = 0.508 (Fisher's exact test), Q value = 0.53

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 44
subtype2 1 21
subtype3 0 46
subtype4 2 37
subtype5 1 24
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 109 51 98
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.29 (logrank test), Q value = 0.34

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

nPatients nDeath Duration Range (Median), Month
ALL 258 95 0.5 - 188.2 (26.4)
subtype1 109 44 0.6 - 188.2 (25.4)
subtype2 51 16 3.1 - 124.4 (26.2)
subtype3 98 35 0.5 - 123.8 (27.4)

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 = 0.178 (Kruskal-Wallis (anova)), Q value = 0.24

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

nPatients Mean (Std.Dev)
ALL 257 61.0 (14.7)
subtype1 109 60.7 (16.9)
subtype2 51 64.1 (13.2)
subtype3 97 59.6 (12.5)

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 = 0.00108 (Fisher's exact test), Q value = 0.0029

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 44 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 1 3 2 0 1 0 6 0 1 1 0 0 8 1 2 1 6 2 15 0 1 4 3 1 0 36 0 1 2 3 1 2 4
subtype2 0 2 0 0 0 0 1 1 0 0 0 0 0 0 2 1 6 2 15 0 0 1 0 0 0 13 0 1 2 1 0 2 1
subtype3 0 2 0 1 1 1 21 1 0 1 1 2 3 1 0 0 4 1 14 4 1 0 5 1 1 25 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 = 0.0401 (Fisher's exact test), Q value = 0.066

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

nPatients FEMALE MALE
ALL 140 118
subtype1 52 57
subtype2 25 26
subtype3 63 35

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 181 70
subtype1 81 28
subtype2 29 21
subtype3 71 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 = 5.3e-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 24 29 2 2 6 20
subtype1 41 2 0 17 6 8 14 2 2 6 11
subtype2 15 0 0 6 3 9 12 0 0 0 6
subtype3 3 0 1 81 0 7 3 0 0 0 3

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0802 (Fisher's exact test), Q value = 0.12

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

nPatients R0 R1 R2 RX
ALL 154 68 9 26
subtype1 57 36 6 10
subtype2 29 13 0 8
subtype3 68 19 3 8

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.494 (Fisher's exact test), Q value = 0.52

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 226
subtype1 1 6 101
subtype2 1 4 44
subtype3 4 8 81

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.23 (Fisher's exact test), Q value = 0.3

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 94
subtype2 2 47
subtype3 0 81

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 81 53 101 23
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.299 (logrank test), Q value = 0.35

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

nPatients nDeath Duration Range (Median), Month
ALL 258 95 0.5 - 188.2 (26.4)
subtype1 81 32 0.6 - 143.4 (24.7)
subtype2 53 17 0.7 - 124.4 (29.0)
subtype3 101 35 0.5 - 123.8 (26.9)
subtype4 23 11 1.1 - 188.2 (19.7)

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 = 0.00035 (Kruskal-Wallis (anova)), Q value = 0.0011

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

nPatients Mean (Std.Dev)
ALL 257 61.0 (14.7)
subtype1 81 64.4 (13.9)
subtype2 53 64.0 (13.6)
subtype3 100 59.5 (12.6)
subtype4 23 47.8 (20.0)

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-05 (Fisher's exact test), Q value = 1e-04

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 44 4 2 5 8 2 1 74 3 2 5 4 1 7 5
subtype1 0 1 0 0 0 0 4 1 0 0 0 0 4 1 1 1 3 2 14 0 1 4 1 1 0 33 0 1 2 1 0 2 3
subtype2 0 2 0 0 0 1 1 0 0 1 0 0 0 0 0 1 9 2 13 0 0 0 0 0 0 13 0 1 2 2 0 2 2
subtype3 0 3 0 1 1 0 21 1 0 1 1 2 4 1 1 0 4 1 14 4 1 0 5 1 1 26 3 0 1 0 0 3 0
subtype4 1 1 2 0 1 0 2 0 1 0 0 0 3 0 2 0 0 0 3 0 0 1 2 0 0 2 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 = 0.0184 (Fisher's exact test), Q value = 0.032

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

nPatients FEMALE MALE
ALL 140 118
subtype1 34 47
subtype2 27 26
subtype3 63 38
subtype4 16 7

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.121 (Fisher's exact test), Q value = 0.18

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

nPatients NO YES
ALL 181 70
subtype1 60 21
subtype2 31 21
subtype3 74 21
subtype4 16 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 = 5.3e-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 24 29 2 2 6 20
subtype1 36 2 0 12 0 9 15 0 0 0 7
subtype2 15 0 0 7 5 7 9 1 0 0 9
subtype3 4 0 1 81 0 8 4 0 0 0 3
subtype4 4 0 0 4 4 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 = 7e-05 (Fisher's exact test), Q value = 3e-04

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

nPatients R0 R1 R2 RX
ALL 154 68 9 26
subtype1 39 35 3 4
subtype2 31 10 0 11
subtype3 70 21 3 7
subtype4 14 2 3 4

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.355 (Fisher's exact test), Q value = 0.4

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 18 226
subtype1 1 2 76
subtype2 1 5 46
subtype3 4 9 83
subtype4 0 2 21

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0107 (Fisher's exact test), Q value = 0.022

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 222
subtype1 1 75
subtype2 2 49
subtype3 0 84
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
Number of samples 85 65 47
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.443 (logrank test), Q value = 0.49

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

nPatients nDeath Duration Range (Median), Month
ALL 197 74 0.5 - 188.2 (25.2)
subtype1 85 34 0.6 - 188.2 (21.7)
subtype2 65 26 0.5 - 120.2 (25.2)
subtype3 47 14 3.1 - 124.4 (25.7)

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 = 0.163 (Kruskal-Wallis (anova)), Q value = 0.22

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

nPatients Mean (Std.Dev)
ALL 196 61.4 (14.8)
subtype1 85 60.1 (17.0)
subtype2 64 60.0 (12.4)
subtype3 47 65.5 (12.9)

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 = 5.3e-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 41 2 2 3 5 1 52 2 2 4 2 5 4
subtype1 1 3 1 0 1 1 5 0 1 0 0 5 1 2 0 3 2 12 0 1 3 3 1 33 0 0 1 2 1 2
subtype2 0 2 0 1 1 0 16 0 0 1 2 3 0 1 0 2 1 11 2 1 0 2 0 16 2 0 0 0 1 0
subtype3 0 2 0 0 0 0 0 1 0 0 0 0 1 1 1 8 1 18 0 0 0 0 0 3 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 = 0.143 (Fisher's exact test), Q value = 0.2

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

nPatients FEMALE MALE
ALL 111 86
subtype1 43 42
subtype2 43 22
subtype3 25 22

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 = 0.0151 (Fisher's exact test), Q value = 0.029

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

nPatients NO YES
ALL 134 59
subtype1 65 20
subtype2 45 17
subtype3 24 22

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 = 5.3e-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 20 28 2 1 5 16
subtype1 35 2 15 4 6 9 2 1 5 6
subtype2 4 0 52 1 5 3 0 0 0 0
subtype3 4 0 5 3 9 16 0 0 0 10

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 = 0.0692 (Fisher's exact test), Q value = 0.11

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

nPatients R0 R1 R2 RX
ALL 115 55 8 18
subtype1 40 31 6 8
subtype2 43 13 2 7
subtype3 32 11 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 = 0.276 (Fisher's exact test), Q value = 0.34

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 13 172
subtype1 1 3 80
subtype2 1 6 54
subtype3 2 4 38

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 = 0.285 (Fisher's exact test), Q value = 0.34

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 2 73
subtype2 0 58
subtype3 2 41

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 6 7 8
Number of samples 24 33 38 24 11 22 22 23
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.087 (logrank test), Q value = 0.13

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

nPatients nDeath Duration Range (Median), Month
ALL 197 74 0.5 - 188.2 (25.2)
subtype1 24 6 4.8 - 124.4 (34.9)
subtype2 33 12 1.1 - 124.7 (32.7)
subtype3 38 16 0.5 - 120.2 (36.8)
subtype4 24 11 0.7 - 102.0 (22.3)
subtype5 11 1 9.0 - 53.3 (22.6)
subtype6 22 11 0.6 - 143.4 (18.1)
subtype7 22 6 3.1 - 106.5 (17.2)
subtype8 23 11 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 = 0.0112 (Kruskal-Wallis (anova)), Q value = 0.023

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

nPatients Mean (Std.Dev)
ALL 196 61.4 (14.8)
subtype1 24 59.4 (11.5)
subtype2 33 63.3 (13.1)
subtype3 38 60.1 (11.6)
subtype4 24 66.5 (13.0)
subtype5 10 56.0 (11.8)
subtype6 22 66.7 (15.4)
subtype7 22 66.6 (14.1)
subtype8 23 49.7 (20.5)

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 = 5.3e-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 41 2 2 3 5 1 52 2 2 4 2 5 4
subtype1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 10 0 0 1 0 0 4 0 0 0 1 1 1
subtype2 0 1 0 0 0 0 0 0 0 0 0 2 1 0 0 1 1 1 0 1 0 1 1 21 0 0 1 0 1 0
subtype3 0 0 0 1 1 0 9 0 0 0 1 1 0 0 0 2 0 6 1 1 0 2 0 10 2 0 0 0 1 0
subtype4 0 4 0 0 0 0 2 0 0 0 0 0 1 0 0 3 0 9 1 0 0 0 0 3 0 1 0 0 0 0
subtype5 0 0 0 0 0 0 4 0 0 1 1 2 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
subtype6 0 1 0 0 0 0 2 0 0 0 0 0 0 0 1 1 1 8 0 0 1 0 0 7 0 0 0 0 0 0
subtype7 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 5 1 4 0 0 0 0 0 2 0 1 3 0 2 2
subtype8 1 1 1 0 1 0 3 0 1 0 0 2 0 2 0 0 0 3 0 0 1 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 = 0.00227 (Fisher's exact test), Q value = 0.0057

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

nPatients FEMALE MALE
ALL 111 86
subtype1 11 13
subtype2 11 22
subtype3 28 10
subtype4 14 10
subtype5 7 4
subtype6 9 13
subtype7 12 10
subtype8 19 4

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 = 0.0172 (Fisher's exact test), Q value = 0.032

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

nPatients NO YES
ALL 134 59
subtype1 12 11
subtype2 27 6
subtype3 28 9
subtype4 13 10
subtype5 10 0
subtype6 16 6
subtype7 11 11
subtype8 17 6

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 = 5.3e-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 20 28 2 1 5 16
subtype1 7 0 3 1 4 7 0 0 0 2
subtype2 22 0 1 0 5 4 0 0 0 1
subtype3 0 0 38 0 0 0 0 0 0 0
subtype4 3 0 5 0 6 6 0 0 0 4
subtype5 1 0 10 0 0 0 0 0 0 0
subtype6 4 2 9 0 2 2 0 0 0 3
subtype7 2 0 0 3 3 8 0 0 0 6
subtype8 4 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 = 0.0187 (Fisher's exact test), Q value = 0.032

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

nPatients R0 R1 R2 RX
ALL 115 55 8 18
subtype1 13 4 2 5
subtype2 17 15 1 0
subtype3 28 8 0 2
subtype4 13 8 2 1
subtype5 8 2 0 1
subtype6 8 11 1 2
subtype7 15 3 0 3
subtype8 13 4 2 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 = 0.0994 (Fisher's exact test), Q value = 0.15

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 13 172
subtype1 0 2 22
subtype2 0 0 33
subtype3 0 4 32
subtype4 1 2 19
subtype5 1 2 7
subtype6 0 0 21
subtype7 1 1 18
subtype8 1 2 20

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 = 0.273 (Fisher's exact test), Q value = 0.34

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 0 23
subtype2 1 31
subtype3 0 34
subtype4 0 20
subtype5 0 10
subtype6 0 19
subtype7 2 19
subtype8 1 16

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/20140640/SARC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SARC-TP/19775498/SARC-TP.merged_data.txt

  • Number of patients = 260

  • 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle