Correlation between copy number variations of arm-level result and selected clinical features
Colon Adenocarcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1JM27TN
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JM27TN
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between subtypes identified by 76 different clustering approaches and 9 clinical features across 413 patients, 19 significant findings detected with Q value < 0.25.

  • 2 subtypes identified in current cancer cohort by '1p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '1q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3p gain mutation analysis'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '3q gain mutation analysis'. These subtypes correlate to 'Time to Death' and 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '4p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7p gain mutation analysis'. These subtypes correlate to 'TUMOR.STAGE'.

  • 2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '13q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '14q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '15q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '17p gain mutation analysis'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '17q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18p gain mutation analysis'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '18q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20p gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '20q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '21q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by 'Xq gain mutation analysis'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '1p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '1q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2p loss mutation analysis'. These subtypes correlate to 'Time to Death'.

  • 2 subtypes identified in current cancer cohort by '2q loss mutation analysis'. These subtypes correlate to 'Time to Death'.

  • 2 subtypes identified in current cancer cohort by '3p loss mutation analysis'. These subtypes correlate to 'Time to Death'.

  • 2 subtypes identified in current cancer cohort by '3q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6q loss mutation analysis'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 2 subtypes identified in current cancer cohort by '8p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '13q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '14q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '15q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '17p loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'TUMOR.STAGE'.

  • 2 subtypes identified in current cancer cohort by '17q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18p loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '18q loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '19p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '21q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '22q loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by 'Xq loss mutation analysis'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 76 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 19 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER HISTOLOGICAL
TYPE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test t-test Fisher's exact test Fisher's exact test Chi-square test Chi-square test Chi-square test Chi-square test Fisher's exact test
1p gain 0.44
(1.00) 		0.664
(1.00) 		1
(1.00) 		1
(1.00) 		0.424
(1.00) 		0.422
(1.00) 		0.776
(1.00) 		0.506
(1.00) 		1
(1.00)
1q gain 0.889
(1.00) 		0.466
(1.00) 		0.0868
(1.00) 		0.173
(1.00) 		0.463
(1.00) 		0.339
(1.00) 		0.23
(1.00) 		0.0613
(1.00) 		1
(1.00)
2p gain 0.604
(1.00) 		0.61
(1.00) 		0.869
(1.00) 		0.0447
(1.00) 		0.278
(1.00) 		0.0553
(1.00) 		0.895
(1.00) 		0.0325
(1.00) 		0.264
(1.00)
2q gain 0.906
(1.00) 		0.781
(1.00) 		0.738
(1.00) 		0.0436
(1.00) 		0.31
(1.00) 		0.0127
(1.00) 		0.786
(1.00) 		0.0112
(1.00) 		0.258
(1.00)
3p gain 0.00121
(0.799) 		0.152
(1.00) 		0.496
(1.00) 		1
(1.00) 		0.905
(1.00) 		0.345
(1.00) 		0.000107
(0.0717) 		0.406
(1.00) 		1
(1.00)
3q gain 4.78e-05
(0.0322) 		0.588
(1.00) 		0.342
(1.00) 		0.783
(1.00) 		0.975
(1.00) 		0.0857
(1.00) 		0.000364
(0.242) 		0.0574
(1.00) 		1
(1.00)
4p gain 0.457
(1.00) 		0.569
(1.00) 		1
(1.00) 		0.346
(1.00) 		0.713
(1.00) 		0.0938
(1.00) 		0.000376
(0.25) 		0.268
(1.00) 		1
(1.00)
4q gain 0.445
(1.00) 		0.856
(1.00) 		0.668
(1.00) 		0.508
(1.00) 		0.684
(1.00) 		0.176
(1.00) 		0.291
(1.00) 		0.0877
(1.00) 		1
(1.00)
5p gain 0.806
(1.00) 		0.0805
(1.00) 		0.239
(1.00) 		0.453
(1.00) 		0.605
(1.00) 		0.172
(1.00) 		0.347
(1.00) 		0.67
(1.00) 		1
(1.00)
5q gain 0.609
(1.00) 		0.285
(1.00) 		0.374
(1.00) 		0.335
(1.00) 		0.765
(1.00) 		0.0806
(1.00) 		0.742
(1.00) 		0.738
(1.00) 		1
(1.00)
6p gain 0.747
(1.00) 		0.574
(1.00) 		0.763
(1.00) 		0.506
(1.00) 		0.371
(1.00) 		0.0114
(1.00) 		0.995
(1.00) 		0.158
(1.00) 		1
(1.00)
6q gain 0.676
(1.00) 		0.9
(1.00) 		0.755
(1.00) 		1
(1.00) 		0.483
(1.00) 		0.0104
(1.00) 		0.969
(1.00) 		0.0807
(1.00) 		1
(1.00)
7p gain 0.252
(1.00) 		0.362
(1.00) 		1
(1.00) 		0.00119
(0.784) 		0.764
(1.00) 		0.00448
(1.00) 		0.032
(1.00) 		7e-05
(0.0471) 		0.113
(1.00)
7q gain 0.313
(1.00) 		0.448
(1.00) 		0.921
(1.00) 		0.0115
(1.00) 		0.54
(1.00) 		0.207
(1.00) 		0.0184
(1.00) 		0.00471
(1.00) 		0.577
(1.00)
8p gain 0.845
(1.00) 		0.199
(1.00) 		0.618
(1.00) 		1
(1.00) 		0.236
(1.00) 		0.414
(1.00) 		0.935
(1.00) 		0.621
(1.00) 		0.477
(1.00)
8q gain 0.61
(1.00) 		0.0678
(1.00) 		0.267
(1.00) 		0.00673
(1.00) 		0.0364
(1.00) 		0.707
(1.00) 		0.3
(1.00) 		0.386
(1.00) 		0.564
(1.00)
9p gain 0.0786
(1.00) 		0.544
(1.00) 		0.347
(1.00) 		0.358
(1.00) 		0.611
(1.00) 		0.608
(1.00) 		0.786
(1.00) 		0.534
(1.00) 		1
(1.00)
9q gain 0.0609
(1.00) 		0.608
(1.00) 		0.179
(1.00) 		0.622
(1.00) 		0.23
(1.00) 		0.273
(1.00) 		0.799
(1.00) 		0.494
(1.00) 		1
(1.00)
10p gain 0.00999
(1.00) 		0.098
(1.00) 		0.813
(1.00) 		0.711
(1.00) 		0.107
(1.00) 		0.00768
(1.00) 		0.622
(1.00) 		0.15
(1.00) 		1
(1.00)
10q gain 0.788
(1.00) 		0.0299
(1.00) 		1
(1.00) 		0.232
(1.00) 		0.359
(1.00) 		0.0188
(1.00) 		0.86
(1.00) 		0.0802
(1.00) 		1
(1.00)
11p gain 0.412
(1.00) 		0.198
(1.00) 		0.631
(1.00) 		0.147
(1.00) 		0.285
(1.00) 		0.114
(1.00) 		0.93
(1.00) 		0.112
(1.00) 		1
(1.00)
11q gain 0.203
(1.00) 		0.0508
(1.00) 		0.828
(1.00) 		0.0957
(1.00) 		0.353
(1.00) 		0.0574
(1.00) 		0.864
(1.00) 		0.0161
(1.00) 		1
(1.00)
12p gain 0.659
(1.00) 		0.0837
(1.00) 		1
(1.00) 		0.69
(1.00) 		0.47
(1.00) 		0.0372
(1.00) 		0.446
(1.00) 		0.0418
(1.00) 		1
(1.00)
12q gain 0.845
(1.00) 		0.0503
(1.00) 		0.889
(1.00) 		0.677
(1.00) 		0.58
(1.00) 		0.0597
(1.00) 		0.188
(1.00) 		0.0249
(1.00) 		1
(1.00)
13q gain 0.76
(1.00) 		0.942
(1.00) 		0.376
(1.00) 		4e-06
(0.00271) 		0.95
(1.00) 		0.0354
(1.00) 		0.127
(1.00) 		0.0118
(1.00) 		0.249
(1.00)
14q gain 0.506
(1.00) 		0.922
(1.00) 		0.43
(1.00) 		0.43
(1.00) 		0.486
(1.00) 		0.88
(1.00) 		0.00355
(1.00) 		0.662
(1.00) 		1
(1.00)
15q gain 0.493
(1.00) 		0.709
(1.00) 		1
(1.00) 		1
(1.00) 		0.85
(1.00) 		0.536
(1.00) 		0.825
(1.00) 		0.7
(1.00) 		1
(1.00)
16p gain 0.938
(1.00) 		0.205
(1.00) 		0.79
(1.00) 		0.0179
(1.00) 		0.604
(1.00) 		0.0103
(1.00) 		0.662
(1.00) 		0.00724
(1.00) 		0.413
(1.00)
16q gain 0.738
(1.00) 		0.107
(1.00) 		1
(1.00) 		0.027
(1.00) 		0.608
(1.00) 		0.00172
(1.00) 		0.633
(1.00) 		0.000751
(0.496) 		0.408
(1.00)
17p gain 0.206
(1.00) 		0.863
(1.00) 		0.739
(1.00) 		0.613
(1.00) 		0.495
(1.00) 		0.725
(1.00) 		0.000127
(0.0847) 		0.204
(1.00) 		1
(1.00)
17q gain 0.0544
(1.00) 		0.335
(1.00) 		0.539
(1.00) 		0.0648
(1.00) 		0.0959
(1.00) 		0.305
(1.00) 		0.00607
(1.00) 		0.435
(1.00) 		0.31
(1.00)
18p gain 0.319
(1.00) 		0.816
(1.00) 		1
(1.00) 		0.0351
(1.00) 		0.905
(1.00) 		0.00659
(1.00) 		6.1e-06
(0.00412) 		0.749
(1.00) 		1
(1.00)
18q gain 0.699
(1.00) 		0.331
(1.00) 		0.512
(1.00) 		0.102
(1.00) 		0.644
(1.00) 		0.167
(1.00) 		0.909
(1.00) 		0.935
(1.00) 		1
(1.00)
19p gain 0.605
(1.00) 		0.445
(1.00) 		0.858
(1.00) 		0.0669
(1.00) 		0.501
(1.00) 		0.333
(1.00) 		0.168
(1.00) 		0.882
(1.00) 		1
(1.00)
19q gain 0.285
(1.00) 		0.403
(1.00) 		0.869
(1.00) 		0.631
(1.00) 		0.939
(1.00) 		0.979
(1.00) 		0.589
(1.00) 		0.863
(1.00) 		1
(1.00)
20p gain 0.287
(1.00) 		0.283
(1.00) 		0.0599
(1.00) 		6.4e-05
(0.0431) 		0.582
(1.00) 		0.572
(1.00) 		0.0382
(1.00) 		0.0784
(1.00) 		0.594
(1.00)
20q gain 0.614
(1.00) 		0.625
(1.00) 		0.257
(1.00) 		2.87e-08
(1.96e-05) 		0.226
(1.00) 		0.228
(1.00) 		0.17
(1.00) 		0.0232
(1.00) 		1
(1.00)
21q gain 0.152
(1.00) 		0.00583
(1.00) 		0.762
(1.00) 		1
(1.00) 		0.0063
(1.00) 		0.176
(1.00) 		0.799
(1.00) 		0.21
(1.00) 		1
(1.00)
Xq gain 0.0145
(1.00) 		0.315
(1.00) 		0.559
(1.00) 		0.664
(1.00) 		0.923
(1.00) 		0.0763
(1.00) 		1.5e-07
(0.000102) 		0.623
(1.00) 		1
(1.00)
1p loss 0.0103
(1.00) 		0.507
(1.00) 		1
(1.00) 		0.122
(1.00) 		0.801
(1.00) 		0.0278
(1.00) 		0.0303
(1.00) 		0.0301
(1.00) 		1
(1.00)
1q loss 0.558
(1.00) 		0.256
(1.00) 		0.631
(1.00) 		1
(1.00) 		0.721
(1.00) 		0.472
(1.00) 		0.424
(1.00) 		0.663
(1.00) 		1
(1.00)
2p loss 1.75e-11
(1.2e-08) 		0.693
(1.00) 		0.626
(1.00) 		1
(1.00) 		0.609
(1.00) 		0.901
(1.00) 		0.877
(1.00) 		0.582
(1.00) 		1
(1.00)
2q loss 2.91e-07
(0.000197) 		0.81
(1.00) 		0.221
(1.00) 		1
(1.00) 		0.797
(1.00) 		0.831
(1.00) 		0.77
(1.00) 		0.4
(1.00) 		1
(1.00)
3p loss 7.69e-05
(0.0516) 		0.754
(1.00) 		0.325
(1.00) 		0.233
(1.00) 		0.108
(1.00) 		0.0903
(1.00) 		0.026
(1.00) 		0.0297
(1.00) 		1
(1.00)
3q loss 0.0351
(1.00) 		0.0171
(1.00) 		0.0406
(1.00) 		0.38
(1.00) 		0.714
(1.00) 		0.722
(1.00) 		0.19
(1.00) 		0.114
(1.00) 		1
(1.00)
4p loss 0.00142
(0.93) 		0.764
(1.00) 		0.807
(1.00) 		0.0103
(1.00) 		0.795
(1.00) 		0.0194
(1.00) 		0.00219
(1.00) 		0.00403
(1.00) 		0.495
(1.00)
4q loss 0.0221
(1.00) 		0.478
(1.00) 		0.596
(1.00) 		0.0179
(1.00) 		0.92
(1.00) 		0.114
(1.00) 		0.125
(1.00) 		0.0822
(1.00) 		0.418
(1.00)
5p loss 0.761
(1.00) 		0.819
(1.00) 		1
(1.00) 		0.227
(1.00) 		0.712
(1.00) 		0.182
(1.00) 		0.822
(1.00) 		0.101
(1.00) 		0.171
(1.00)
5q loss 0.341
(1.00) 		0.867
(1.00) 		0.0602
(1.00) 		0.0433
(1.00) 		0.72
(1.00) 		0.00204
(1.00) 		0.729
(1.00) 		0.305
(1.00) 		0.252
(1.00)
6p loss 0.755
(1.00) 		0.625
(1.00) 		0.777
(1.00) 		1
(1.00) 		0.87
(1.00) 		0.707
(1.00) 		0.366
(1.00) 		0.884
(1.00) 		1
(1.00)
6q loss 0.0115
(1.00) 		0.368
(1.00) 		1
(1.00) 		0.748
(1.00) 		0.615
(1.00) 		0.0263
(1.00) 		0.000182
(0.121) 		0.0906
(1.00) 		1
(1.00)
8p loss 0.61
(1.00) 		0.641
(1.00) 		0.238
(1.00) 		0.081
(1.00) 		0.743
(1.00) 		0.0204
(1.00) 		0.118
(1.00) 		0.102
(1.00) 		0.536
(1.00)
8q loss 0.884
(1.00) 		0.21
(1.00) 		0.293
(1.00) 		1
(1.00) 		0.556
(1.00) 		0.311
(1.00) 		0.674
(1.00) 		0.123
(1.00) 		1
(1.00)
9p loss 0.21
(1.00) 		0.0653
(1.00) 		1
(1.00) 		0.556
(1.00) 		0.228
(1.00) 		0.823
(1.00) 		0.919
(1.00) 		0.816
(1.00) 		1
(1.00)
9q loss 0.736
(1.00) 		0.0045
(1.00) 		0.311
(1.00) 		0.227
(1.00) 		0.0084
(1.00) 		0.633
(1.00) 		0.926
(1.00) 		0.276
(1.00) 		1
(1.00)
10p loss 0.811
(1.00) 		0.321
(1.00) 		0.514
(1.00) 		1
(1.00) 		0.221
(1.00) 		0.627
(1.00) 		0.104
(1.00) 		0.0738
(1.00) 		1
(1.00)
10q loss 0.28
(1.00) 		0.178
(1.00) 		0.529
(1.00) 		1
(1.00) 		0.564
(1.00) 		0.511
(1.00) 		0.2
(1.00) 		0.255
(1.00) 		1
(1.00)
11p loss 0.235
(1.00) 		0.207
(1.00) 		1
(1.00) 		0.335
(1.00) 		0.89
(1.00) 		0.567
(1.00) 		0.736
(1.00) 		0.421
(1.00) 		1
(1.00)
11q loss 0.724
(1.00) 		0.285
(1.00) 		0.426
(1.00) 		0.556
(1.00) 		0.423
(1.00) 		0.155
(1.00) 		0.455
(1.00) 		0.389
(1.00) 		1
(1.00)
12p loss 0.278
(1.00) 		0.775
(1.00) 		0.374
(1.00) 		0.0918
(1.00) 		0.212
(1.00) 		0.0654
(1.00) 		0.797
(1.00) 		0.259
(1.00) 		0.145
(1.00)
12q loss 0.389
(1.00) 		0.941
(1.00) 		0.256
(1.00) 		0.0904
(1.00) 		0.365
(1.00) 		0.117
(1.00) 		0.742
(1.00) 		0.223
(1.00) 		1
(1.00)
13q loss 0.237
(1.00) 		0.627
(1.00) 		1
(1.00) 		0.574
(1.00) 		0.175
(1.00) 		0.0715
(1.00) 		0.496
(1.00) 		0.296
(1.00) 		1
(1.00)
14q loss 0.345
(1.00) 		0.939
(1.00) 		0.64
(1.00) 		0.000719
(0.476) 		0.895
(1.00) 		0.0825
(1.00) 		0.266
(1.00) 		0.28
(1.00) 		1
(1.00)
15q loss 0.365
(1.00) 		0.424
(1.00) 		0.908
(1.00) 		0.00173
(1.00) 		0.72
(1.00) 		0.58
(1.00) 		0.215
(1.00) 		0.19
(1.00) 		0.562
(1.00)
16p loss 0.977
(1.00) 		0.999
(1.00) 		0.315
(1.00) 		0.337
(1.00) 		0.947
(1.00) 		0.924
(1.00) 		0.993
(1.00) 		0.516
(1.00) 		1
(1.00)
16q loss 0.478
(1.00) 		0.612
(1.00) 		0.71
(1.00) 		0.232
(1.00) 		0.688
(1.00) 		0.801
(1.00) 		0.98
(1.00) 		0.851
(1.00) 		1
(1.00)
17p loss 0.162
(1.00) 		0.339
(1.00) 		0.191
(1.00) 		5.34e-06
(0.00362) 		0.841
(1.00) 		0.00384
(1.00) 		0.000887
(0.586) 		0.000247
(0.165) 		0.566
(1.00)
17q loss 0.702
(1.00) 		0.544
(1.00) 		0.716
(1.00) 		0.101
(1.00) 		0.698
(1.00) 		0.0599
(1.00) 		0.37
(1.00) 		0.0633
(1.00) 		1
(1.00)
18p loss 0.365
(1.00) 		0.108
(1.00) 		1
(1.00) 		1.03e-09
(7.01e-07) 		0.825
(1.00) 		0.00436
(1.00) 		0.029
(1.00) 		0.000691
(0.458) 		1
(1.00)
18q loss 0.265
(1.00) 		0.119
(1.00) 		0.429
(1.00) 		7.61e-09
(5.19e-06) 		0.936
(1.00) 		0.00271
(1.00) 		0.014
(1.00) 		0.000473
(0.314) 		0.254
(1.00)
19p loss 0.291
(1.00) 		0.5
(1.00) 		0.275
(1.00) 		0.232
(1.00) 		0.462
(1.00) 		0.915
(1.00) 		0.226
(1.00) 		0.219
(1.00) 		1
(1.00)
19q loss 0.291
(1.00) 		0.73
(1.00) 		0.156
(1.00) 		0.232
(1.00) 		0.365
(1.00) 		0.982
(1.00) 		0.166
(1.00) 		0.27
(1.00) 		1
(1.00)
20p loss 0.56
(1.00) 		0.806
(1.00) 		0.59
(1.00) 		0.599
(1.00) 		0.665
(1.00) 		0.545
(1.00) 		0.71
(1.00) 		0.55
(1.00) 		1
(1.00)
21q loss 0.666
(1.00) 		0.675
(1.00) 		0.896
(1.00) 		0.0514
(1.00) 		0.161
(1.00) 		0.398
(1.00) 		0.928
(1.00) 		0.858
(1.00) 		1
(1.00)
22q loss 0.527
(1.00) 		0.105
(1.00) 		0.535
(1.00) 		7.26e-05
(0.0488) 		0.772
(1.00) 		0.035
(1.00) 		0.148
(1.00) 		0.0799
(1.00) 		0.481
(1.00)
Xq loss 0.725
(1.00) 		0.78
(1.00) 		0.668
(1.00) 		0.508
(1.00) 		0.173
(1.00) 		0.176
(1.00) 		0.869
(1.00) 		0.608
(1.00) 		1
(1.00)
Clustering Approach #1: '1p gain mutation analysis'

Table S1.  Get Full Table Description of clustering approach #1: '1p gain mutation analysis'

Cluster Labels 1P GAIN MUTATED 1P GAIN WILD-TYPE
Number of samples 11 402
Clustering Approach #2: '1q gain mutation analysis'

Table S2.  Get Full Table Description of clustering approach #2: '1q gain mutation analysis'

Cluster Labels 1Q GAIN MUTATED 1Q GAIN WILD-TYPE
Number of samples 47 366
Clustering Approach #3: '2p gain mutation analysis'

Table S3.  Get Full Table Description of clustering approach #3: '2p gain mutation analysis'

Cluster Labels 2P GAIN MUTATED 2P GAIN WILD-TYPE
Number of samples 40 373
Clustering Approach #4: '2q gain mutation analysis'

Table S4.  Get Full Table Description of clustering approach #4: '2q gain mutation analysis'

Cluster Labels 2Q GAIN MUTATED 2Q GAIN WILD-TYPE
Number of samples 39 374
Clustering Approach #5: '3p gain mutation analysis'

Table S5.  Get Full Table Description of clustering approach #5: '3p gain mutation analysis'

Cluster Labels 3P GAIN MUTATED 3P GAIN WILD-TYPE
Number of samples 20 393
'3p gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000107 (Chi-square test), Q value = 0.072

Table S6.  Clustering Approach #5: '3p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
3P GAIN MUTATED 12 2 1 1 3
3P GAIN WILD-TYPE 300 47 6 0 33

Figure S1.  Get High-res Image Clustering Approach #5: '3p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #6: '3q gain mutation analysis'

Table S7.  Get Full Table Description of clustering approach #6: '3q gain mutation analysis'

Cluster Labels 3Q GAIN MUTATED 3Q GAIN WILD-TYPE
Number of samples 30 383
'3q gain mutation analysis' versus 'Time to Death'

P value = 4.78e-05 (logrank test), Q value = 0.032

Table S8.  Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 49 0.1 - 135.5 (7.7)
3Q GAIN MUTATED 23 8 0.2 - 46.8 (5.0)
3Q GAIN WILD-TYPE 296 41 0.1 - 135.5 (8.0)

Figure S2.  Get High-res Image Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'3q gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000364 (Chi-square test), Q value = 0.24

Table S9.  Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
3Q GAIN MUTATED 17 5 2 1 4
3Q GAIN WILD-TYPE 295 44 5 0 32

Figure S3.  Get High-res Image Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #7: '4p gain mutation analysis'

Table S10.  Get Full Table Description of clustering approach #7: '4p gain mutation analysis'

Cluster Labels 4P GAIN MUTATED 4P GAIN WILD-TYPE
Number of samples 3 410
Clustering Approach #8: '4q gain mutation analysis'

Table S11.  Get Full Table Description of clustering approach #8: '4q gain mutation analysis'

Cluster Labels 4Q GAIN MUTATED 4Q GAIN WILD-TYPE
Number of samples 5 408
Clustering Approach #9: '5p gain mutation analysis'

Table S12.  Get Full Table Description of clustering approach #9: '5p gain mutation analysis'

Cluster Labels 5P GAIN MUTATED 5P GAIN WILD-TYPE
Number of samples 39 374
Clustering Approach #10: '5q gain mutation analysis'

Table S13.  Get Full Table Description of clustering approach #10: '5q gain mutation analysis'

Cluster Labels 5Q GAIN MUTATED 5Q GAIN WILD-TYPE
Number of samples 21 392
Clustering Approach #11: '6p gain mutation analysis'

Table S14.  Get Full Table Description of clustering approach #11: '6p gain mutation analysis'

Cluster Labels 6P GAIN MUTATED 6P GAIN WILD-TYPE
Number of samples 50 363
Clustering Approach #12: '6q gain mutation analysis'

Table S15.  Get Full Table Description of clustering approach #12: '6q gain mutation analysis'

Cluster Labels 6Q GAIN MUTATED 6Q GAIN WILD-TYPE
Number of samples 45 368
Clustering Approach #13: '7p gain mutation analysis'

Table S16.  Get Full Table Description of clustering approach #13: '7p gain mutation analysis'

Cluster Labels 7P GAIN MUTATED 7P GAIN WILD-TYPE
Number of samples 200 213
'7p gain mutation analysis' versus 'TUMOR.STAGE'

P value = 7e-05 (Chi-square test), Q value = 0.047

Table S17.  Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 67 157 116 56
7P GAIN MUTATED 31 55 68 36
7P GAIN WILD-TYPE 36 102 48 20

Figure S4.  Get High-res Image Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #14: '7q gain mutation analysis'

Table S18.  Get Full Table Description of clustering approach #14: '7q gain mutation analysis'

Cluster Labels 7Q GAIN MUTATED 7Q GAIN WILD-TYPE
Number of samples 175 238
Clustering Approach #15: '8p gain mutation analysis'

Table S19.  Get Full Table Description of clustering approach #15: '8p gain mutation analysis'

Cluster Labels 8P GAIN MUTATED 8P GAIN WILD-TYPE
Number of samples 80 333
Clustering Approach #16: '8q gain mutation analysis'

Table S20.  Get Full Table Description of clustering approach #16: '8q gain mutation analysis'

Cluster Labels 8Q GAIN MUTATED 8Q GAIN WILD-TYPE
Number of samples 162 251
Clustering Approach #17: '9p gain mutation analysis'

Table S21.  Get Full Table Description of clustering approach #17: '9p gain mutation analysis'

Cluster Labels 9P GAIN MUTATED 9P GAIN WILD-TYPE
Number of samples 46 367
Clustering Approach #18: '9q gain mutation analysis'

Table S22.  Get Full Table Description of clustering approach #18: '9q gain mutation analysis'

Cluster Labels 9Q GAIN MUTATED 9Q GAIN WILD-TYPE
Number of samples 39 374
Clustering Approach #19: '10p gain mutation analysis'

Table S23.  Get Full Table Description of clustering approach #19: '10p gain mutation analysis'

Cluster Labels 10P GAIN MUTATED 10P GAIN WILD-TYPE
Number of samples 18 395
Clustering Approach #20: '10q gain mutation analysis'

Table S24.  Get Full Table Description of clustering approach #20: '10q gain mutation analysis'

Cluster Labels 10Q GAIN MUTATED 10Q GAIN WILD-TYPE
Number of samples 7 406
Clustering Approach #21: '11p gain mutation analysis'

Table S25.  Get Full Table Description of clustering approach #21: '11p gain mutation analysis'

Cluster Labels 11P GAIN MUTATED 11P GAIN WILD-TYPE
Number of samples 18 395
Clustering Approach #22: '11q gain mutation analysis'

Table S26.  Get Full Table Description of clustering approach #22: '11q gain mutation analysis'

Cluster Labels 11Q GAIN MUTATED 11Q GAIN WILD-TYPE
Number of samples 22 391
Clustering Approach #23: '12p gain mutation analysis'

Table S27.  Get Full Table Description of clustering approach #23: '12p gain mutation analysis'

Cluster Labels 12P GAIN MUTATED 12P GAIN WILD-TYPE
Number of samples 66 347
Clustering Approach #24: '12q gain mutation analysis'

Table S28.  Get Full Table Description of clustering approach #24: '12q gain mutation analysis'

Cluster Labels 12Q GAIN MUTATED 12Q GAIN WILD-TYPE
Number of samples 60 353
Clustering Approach #25: '13q gain mutation analysis'

Table S29.  Get Full Table Description of clustering approach #25: '13q gain mutation analysis'

Cluster Labels 13Q GAIN MUTATED 13Q GAIN WILD-TYPE
Number of samples 211 202
'13q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 4e-06 (Fisher's exact test), Q value = 0.0027

Table S30.  Clustering Approach #25: '13q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
13Q GAIN MUTATED 198 12
13Q GAIN WILD-TYPE 158 42

Figure S5.  Get High-res Image Clustering Approach #25: '13q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #26: '14q gain mutation analysis'

Table S31.  Get Full Table Description of clustering approach #26: '14q gain mutation analysis'

Cluster Labels 14Q GAIN MUTATED 14Q GAIN WILD-TYPE
Number of samples 15 398
Clustering Approach #27: '15q gain mutation analysis'

Table S32.  Get Full Table Description of clustering approach #27: '15q gain mutation analysis'

Cluster Labels 15Q GAIN MUTATED 15Q GAIN WILD-TYPE
Number of samples 5 408
Clustering Approach #28: '16p gain mutation analysis'

Table S33.  Get Full Table Description of clustering approach #28: '16p gain mutation analysis'

Cluster Labels 16P GAIN MUTATED 16P GAIN WILD-TYPE
Number of samples 67 346
Clustering Approach #29: '16q gain mutation analysis'

Table S34.  Get Full Table Description of clustering approach #29: '16q gain mutation analysis'

Cluster Labels 16Q GAIN MUTATED 16Q GAIN WILD-TYPE
Number of samples 66 347
Clustering Approach #30: '17p gain mutation analysis'

Table S35.  Get Full Table Description of clustering approach #30: '17p gain mutation analysis'

Cluster Labels 17P GAIN MUTATED 17P GAIN WILD-TYPE
Number of samples 9 404
'17p gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000127 (Chi-square test), Q value = 0.085

Table S36.  Clustering Approach #30: '17p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
17P GAIN MUTATED 5 1 2 0 1
17P GAIN WILD-TYPE 307 48 5 1 35

Figure S6.  Get High-res Image Clustering Approach #30: '17p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #31: '17q gain mutation analysis'

Table S37.  Get Full Table Description of clustering approach #31: '17q gain mutation analysis'

Cluster Labels 17Q GAIN MUTATED 17Q GAIN WILD-TYPE
Number of samples 48 365
Clustering Approach #32: '18p gain mutation analysis'

Table S38.  Get Full Table Description of clustering approach #32: '18p gain mutation analysis'

Cluster Labels 18P GAIN MUTATED 18P GAIN WILD-TYPE
Number of samples 15 398
'18p gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 6.1e-06 (Chi-square test), Q value = 0.0041

Table S39.  Clustering Approach #32: '18p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
18P GAIN MUTATED 11 2 0 1 0
18P GAIN WILD-TYPE 301 47 7 0 36

Figure S7.  Get High-res Image Clustering Approach #32: '18p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #33: '18q gain mutation analysis'

Table S40.  Get Full Table Description of clustering approach #33: '18q gain mutation analysis'

Cluster Labels 18Q GAIN MUTATED 18Q GAIN WILD-TYPE
Number of samples 9 404
Clustering Approach #34: '19p gain mutation analysis'

Table S41.  Get Full Table Description of clustering approach #34: '19p gain mutation analysis'

Cluster Labels 19P GAIN MUTATED 19P GAIN WILD-TYPE
Number of samples 34 379
Clustering Approach #35: '19q gain mutation analysis'

Table S42.  Get Full Table Description of clustering approach #35: '19q gain mutation analysis'

Cluster Labels 19Q GAIN MUTATED 19Q GAIN WILD-TYPE
Number of samples 41 372
Clustering Approach #36: '20p gain mutation analysis'

Table S43.  Get Full Table Description of clustering approach #36: '20p gain mutation analysis'

Cluster Labels 20P GAIN MUTATED 20P GAIN WILD-TYPE
Number of samples 188 225
'20p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 6.4e-05 (Fisher's exact test), Q value = 0.043

Table S44.  Clustering Approach #36: '20p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
20P GAIN MUTATED 174 11
20P GAIN WILD-TYPE 182 43

Figure S8.  Get High-res Image Clustering Approach #36: '20p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #37: '20q gain mutation analysis'

Table S45.  Get Full Table Description of clustering approach #37: '20q gain mutation analysis'

Cluster Labels 20Q GAIN MUTATED 20Q GAIN WILD-TYPE
Number of samples 267 146
'20q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 2.87e-08 (Fisher's exact test), Q value = 2e-05

Table S46.  Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
20Q GAIN MUTATED 248 16
20Q GAIN WILD-TYPE 108 38

Figure S9.  Get High-res Image Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #38: '21q gain mutation analysis'

Table S47.  Get Full Table Description of clustering approach #38: '21q gain mutation analysis'

Cluster Labels 21Q GAIN MUTATED 21Q GAIN WILD-TYPE
Number of samples 11 402
Clustering Approach #39: 'Xq gain mutation analysis'

Table S48.  Get Full Table Description of clustering approach #39: 'Xq gain mutation analysis'

Cluster Labels XQ GAIN MUTATED XQ GAIN WILD-TYPE
Number of samples 12 401
'Xq gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 1.5e-07 (Chi-square test), Q value = 1e-04

Table S49.  Clustering Approach #39: 'Xq gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
XQ GAIN MUTATED 8 2 0 1 0
XQ GAIN WILD-TYPE 304 47 7 0 36

Figure S10.  Get High-res Image Clustering Approach #39: 'Xq gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #40: '1p loss mutation analysis'

Table S50.  Get Full Table Description of clustering approach #40: '1p loss mutation analysis'

Cluster Labels 1P LOSS MUTATED 1P LOSS WILD-TYPE
Number of samples 51 362
Clustering Approach #41: '1q loss mutation analysis'

Table S51.  Get Full Table Description of clustering approach #41: '1q loss mutation analysis'

Cluster Labels 1Q LOSS MUTATED 1Q LOSS WILD-TYPE
Number of samples 18 395
Clustering Approach #42: '2p loss mutation analysis'

Table S52.  Get Full Table Description of clustering approach #42: '2p loss mutation analysis'

Cluster Labels 2P LOSS MUTATED 2P LOSS WILD-TYPE
Number of samples 4 409
'2p loss mutation analysis' versus 'Time to Death'

P value = 1.75e-11 (logrank test), Q value = 1.2e-08

Table S53.  Clustering Approach #42: '2p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 49 0.1 - 135.5 (7.7)
2P LOSS MUTATED 3 1 0.3 - 1.0 (1.0)
2P LOSS WILD-TYPE 316 48 0.1 - 135.5 (7.9)

Figure S11.  Get High-res Image Clustering Approach #42: '2p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #43: '2q loss mutation analysis'

Table S54.  Get Full Table Description of clustering approach #43: '2q loss mutation analysis'

Cluster Labels 2Q LOSS MUTATED 2Q LOSS WILD-TYPE
Number of samples 6 407
'2q loss mutation analysis' versus 'Time to Death'

P value = 2.91e-07 (logrank test), Q value = 2e-04

Table S55.  Clustering Approach #43: '2q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 49 0.1 - 135.5 (7.7)
2Q LOSS MUTATED 4 1 0.3 - 1.0 (1.0)
2Q LOSS WILD-TYPE 315 48 0.1 - 135.5 (8.0)

Figure S12.  Get High-res Image Clustering Approach #43: '2q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #44: '3p loss mutation analysis'

Table S56.  Get Full Table Description of clustering approach #44: '3p loss mutation analysis'

Cluster Labels 3P LOSS MUTATED 3P LOSS WILD-TYPE
Number of samples 27 386
'3p loss mutation analysis' versus 'Time to Death'

P value = 7.69e-05 (logrank test), Q value = 0.052

Table S57.  Clustering Approach #44: '3p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 49 0.1 - 135.5 (7.7)
3P LOSS MUTATED 16 5 0.3 - 14.5 (5.3)
3P LOSS WILD-TYPE 303 44 0.1 - 135.5 (7.9)

Figure S13.  Get High-res Image Clustering Approach #44: '3p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #45: '3q loss mutation analysis'

Table S58.  Get Full Table Description of clustering approach #45: '3q loss mutation analysis'

Cluster Labels 3Q LOSS MUTATED 3Q LOSS WILD-TYPE
Number of samples 12 401
Clustering Approach #46: '4p loss mutation analysis'

Table S59.  Get Full Table Description of clustering approach #46: '4p loss mutation analysis'

Cluster Labels 4P LOSS MUTATED 4P LOSS WILD-TYPE
Number of samples 84 329
Clustering Approach #47: '4q loss mutation analysis'

Table S60.  Get Full Table Description of clustering approach #47: '4q loss mutation analysis'

Cluster Labels 4Q LOSS MUTATED 4Q LOSS WILD-TYPE
Number of samples 68 345
Clustering Approach #48: '5p loss mutation analysis'

Table S61.  Get Full Table Description of clustering approach #48: '5p loss mutation analysis'

Cluster Labels 5P LOSS MUTATED 5P LOSS WILD-TYPE
Number of samples 25 388
Clustering Approach #49: '5q loss mutation analysis'

Table S62.  Get Full Table Description of clustering approach #49: '5q loss mutation analysis'

Cluster Labels 5Q LOSS MUTATED 5Q LOSS WILD-TYPE
Number of samples 38 375
Clustering Approach #50: '6p loss mutation analysis'

Table S63.  Get Full Table Description of clustering approach #50: '6p loss mutation analysis'

Cluster Labels 6P LOSS MUTATED 6P LOSS WILD-TYPE
Number of samples 12 401
Clustering Approach #51: '6q loss mutation analysis'

Table S64.  Get Full Table Description of clustering approach #51: '6q loss mutation analysis'

Cluster Labels 6Q LOSS MUTATED 6Q LOSS WILD-TYPE
Number of samples 21 392
'6q loss mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000182 (Chi-square test), Q value = 0.12

Table S65.  Clustering Approach #51: '6q loss mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
6Q LOSS MUTATED 12 4 1 1 3
6Q LOSS WILD-TYPE 300 45 6 0 33

Figure S14.  Get High-res Image Clustering Approach #51: '6q loss mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #52: '8p loss mutation analysis'

Table S66.  Get Full Table Description of clustering approach #52: '8p loss mutation analysis'

Cluster Labels 8P LOSS MUTATED 8P LOSS WILD-TYPE
Number of samples 93 320
Clustering Approach #53: '8q loss mutation analysis'

Table S67.  Get Full Table Description of clustering approach #53: '8q loss mutation analysis'

Cluster Labels 8Q LOSS MUTATED 8Q LOSS WILD-TYPE
Number of samples 8 405
Clustering Approach #54: '9p loss mutation analysis'

Table S68.  Get Full Table Description of clustering approach #54: '9p loss mutation analysis'

Cluster Labels 9P LOSS MUTATED 9P LOSS WILD-TYPE
Number of samples 27 386
Clustering Approach #55: '9q loss mutation analysis'

Table S69.  Get Full Table Description of clustering approach #55: '9q loss mutation analysis'

Cluster Labels 9Q LOSS MUTATED 9Q LOSS WILD-TYPE
Number of samples 26 387
Clustering Approach #56: '10p loss mutation analysis'

Table S70.  Get Full Table Description of clustering approach #56: '10p loss mutation analysis'

Cluster Labels 10P LOSS MUTATED 10P LOSS WILD-TYPE
Number of samples 22 391
Clustering Approach #57: '10q loss mutation analysis'

Table S71.  Get Full Table Description of clustering approach #57: '10q loss mutation analysis'

Cluster Labels 10Q LOSS MUTATED 10Q LOSS WILD-TYPE
Number of samples 24 389
Clustering Approach #58: '11p loss mutation analysis'

Table S72.  Get Full Table Description of clustering approach #58: '11p loss mutation analysis'

Cluster Labels 11P LOSS MUTATED 11P LOSS WILD-TYPE
Number of samples 22 391
Clustering Approach #59: '11q loss mutation analysis'

Table S73.  Get Full Table Description of clustering approach #59: '11q loss mutation analysis'

Cluster Labels 11Q LOSS MUTATED 11Q LOSS WILD-TYPE
Number of samples 27 386
Clustering Approach #60: '12p loss mutation analysis'

Table S74.  Get Full Table Description of clustering approach #60: '12p loss mutation analysis'

Cluster Labels 12P LOSS MUTATED 12P LOSS WILD-TYPE
Number of samples 21 392
Clustering Approach #61: '12q loss mutation analysis'

Table S75.  Get Full Table Description of clustering approach #61: '12q loss mutation analysis'

Cluster Labels 12Q LOSS MUTATED 12Q LOSS WILD-TYPE
Number of samples 20 393
Clustering Approach #62: '13q loss mutation analysis'

Table S76.  Get Full Table Description of clustering approach #62: '13q loss mutation analysis'

Cluster Labels 13Q LOSS MUTATED 13Q LOSS WILD-TYPE
Number of samples 6 407
Clustering Approach #63: '14q loss mutation analysis'

Table S77.  Get Full Table Description of clustering approach #63: '14q loss mutation analysis'

Cluster Labels 14Q LOSS MUTATED 14Q LOSS WILD-TYPE
Number of samples 94 319
Clustering Approach #64: '15q loss mutation analysis'

Table S78.  Get Full Table Description of clustering approach #64: '15q loss mutation analysis'

Cluster Labels 15Q LOSS MUTATED 15Q LOSS WILD-TYPE
Number of samples 99 314
Clustering Approach #65: '16p loss mutation analysis'

Table S79.  Get Full Table Description of clustering approach #65: '16p loss mutation analysis'

Cluster Labels 16P LOSS MUTATED 16P LOSS WILD-TYPE
Number of samples 9 404
Clustering Approach #66: '16q loss mutation analysis'

Table S80.  Get Full Table Description of clustering approach #66: '16q loss mutation analysis'

Cluster Labels 16Q LOSS MUTATED 16Q LOSS WILD-TYPE
Number of samples 7 406
Clustering Approach #67: '17p loss mutation analysis'

Table S81.  Get Full Table Description of clustering approach #67: '17p loss mutation analysis'

Cluster Labels 17P LOSS MUTATED 17P LOSS WILD-TYPE
Number of samples 164 249
'17p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 5.34e-06 (Fisher's exact test), Q value = 0.0036

Table S82.  Clustering Approach #67: '17p loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
17P LOSS MUTATED 157 7
17P LOSS WILD-TYPE 199 47

Figure S15.  Get High-res Image Clustering Approach #67: '17p loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'17p loss mutation analysis' versus 'TUMOR.STAGE'

P value = 0.000247 (Chi-square test), Q value = 0.16

Table S83.  Clustering Approach #67: '17p loss mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 67 157 116 56
17P LOSS MUTATED 24 48 52 35
17P LOSS WILD-TYPE 43 109 64 21

Figure S16.  Get High-res Image Clustering Approach #67: '17p loss mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #68: '17q loss mutation analysis'

Table S84.  Get Full Table Description of clustering approach #68: '17q loss mutation analysis'

Cluster Labels 17Q LOSS MUTATED 17Q LOSS WILD-TYPE
Number of samples 32 381
Clustering Approach #69: '18p loss mutation analysis'

Table S85.  Get Full Table Description of clustering approach #69: '18p loss mutation analysis'

Cluster Labels 18P LOSS MUTATED 18P LOSS WILD-TYPE
Number of samples 209 204
'18p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.03e-09 (Fisher's exact test), Q value = 7e-07

Table S86.  Clustering Approach #69: '18p loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
18P LOSS MUTATED 200 7
18P LOSS WILD-TYPE 156 47

Figure S17.  Get High-res Image Clustering Approach #69: '18p loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #70: '18q loss mutation analysis'

Table S87.  Get Full Table Description of clustering approach #70: '18q loss mutation analysis'

Cluster Labels 18Q LOSS MUTATED 18Q LOSS WILD-TYPE
Number of samples 225 188
'18q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 7.61e-09 (Fisher's exact test), Q value = 5.2e-06

Table S88.  Clustering Approach #70: '18q loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
18Q LOSS MUTATED 214 10
18Q LOSS WILD-TYPE 142 44

Figure S18.  Get High-res Image Clustering Approach #70: '18q loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #71: '19p loss mutation analysis'

Table S89.  Get Full Table Description of clustering approach #71: '19p loss mutation analysis'

Cluster Labels 19P LOSS MUTATED 19P LOSS WILD-TYPE
Number of samples 14 399
Clustering Approach #72: '19q loss mutation analysis'

Table S90.  Get Full Table Description of clustering approach #72: '19q loss mutation analysis'

Cluster Labels 19Q LOSS MUTATED 19Q LOSS WILD-TYPE
Number of samples 13 400
Clustering Approach #73: '20p loss mutation analysis'

Table S91.  Get Full Table Description of clustering approach #73: '20p loss mutation analysis'

Cluster Labels 20P LOSS MUTATED 20P LOSS WILD-TYPE
Number of samples 33 380
Clustering Approach #74: '21q loss mutation analysis'

Table S92.  Get Full Table Description of clustering approach #74: '21q loss mutation analysis'

Cluster Labels 21Q LOSS MUTATED 21Q LOSS WILD-TYPE
Number of samples 71 342
Clustering Approach #75: '22q loss mutation analysis'

Table S93.  Get Full Table Description of clustering approach #75: '22q loss mutation analysis'

Cluster Labels 22Q LOSS MUTATED 22Q LOSS WILD-TYPE
Number of samples 81 332
'22q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 7.26e-05 (Fisher's exact test), Q value = 0.049

Table S94.  Clustering Approach #75: '22q loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
22Q LOSS MUTATED 80 1
22Q LOSS WILD-TYPE 276 53

Figure S19.  Get High-res Image Clustering Approach #75: '22q loss mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #76: 'Xq loss mutation analysis'

Table S95.  Get Full Table Description of clustering approach #76: 'Xq loss mutation analysis'

Cluster Labels XQ LOSS MUTATED XQ LOSS WILD-TYPE
Number of samples 5 408
Methods & Data
Input

  • Cluster data file = broad_values_by_arm.mutsig.cluster.txt

  • Clinical data file = COAD-TP.clin.merged.picked.txt

  • Number of patients = 413

  • Number of clustering approaches = 76

  • Number of selected clinical features = 9

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

Survival analysis

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Fisher's exact test

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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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