Correlation between aggregated molecular cancer subtypes and selected clinical features

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 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.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

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

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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