Correlation between copy number variations of arm-level result and selected clinical features

Colon/Rectal Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WS8RF1

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 78 different clustering approaches and 10 clinical features across 575 patients, 25 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 do not correlate to any clinical features.
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 'HISTOLOGICAL.TYPE' and 'TUMOR.STAGE'.
2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'TUMOR.STAGE'.
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 correlate to 'HISTOLOGICAL.TYPE'.
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 correlate to 'TUMOR.STAGE'.
2 subtypes identified in current cancer cohort by '17p gain mutation analysis'. These subtypes do not correlate to any clinical features.
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 'PRIMARY.SITE.OF.DISEASE' and '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 '22q 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 do not correlate to any clinical features.
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 correlate to 'HISTOLOGICAL.TYPE'.
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 do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '7q loss mutation analysis'. These subtypes do not correlate to any clinical features.
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 correlate to 'HISTOLOGICAL.TYPE'.
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 correlate to 'HISTOLOGICAL.TYPE'.
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'.
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 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '18q loss mutation analysis'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE', 'HISTOLOGICAL.TYPE', and 'TUMOR.STAGE'.
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 do not correlate to any clinical features.
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 78 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 25 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	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	Chi-square test	Fisher's exact test
1p gain	0.85 (1.00)	0.658 (1.00)	1 (1.00)	0.61 (1.00)	0.655 (1.00)	0.828 (1.00)	0.0333 (1.00)	0.813 (1.00)	0.59 (1.00)	1 (1.00)
1q gain	0.656 (1.00)	0.838 (1.00)	0.174 (1.00)	0.148 (1.00)	0.0752 (1.00)	0.203 (1.00)	0.182 (1.00)	0.33 (1.00)	0.0233 (1.00)	1 (1.00)
2p gain	0.328 (1.00)	0.924 (1.00)	0.361 (1.00)	0.173 (1.00)	0.0531 (1.00)	0.532 (1.00)	0.314 (1.00)	0.976 (1.00)	0.137 (1.00)	1 (1.00)
2q gain	0.384 (1.00)	0.448 (1.00)	0.169 (1.00)	0.217 (1.00)	0.036 (1.00)	0.748 (1.00)	0.405 (1.00)	0.9 (1.00)	0.141 (1.00)	1 (1.00)
3p gain	0.0324 (1.00)	0.31 (1.00)	0.0194 (1.00)	0.301 (1.00)	0.0318 (1.00)	0.879 (1.00)	0.634 (1.00)	0.00172 (1.00)	0.424 (1.00)	0.444 (1.00)
3q gain	0.000218 (0.165)	0.644 (1.00)	0.00881 (1.00)	0.148 (1.00)	0.00797 (1.00)	0.91 (1.00)	0.0331 (1.00)	0.000283 (0.214)	0.0225 (1.00)	0.198 (1.00)
4p gain	0.421 (1.00)	0.885 (1.00)	0.0537 (1.00)	0.691 (1.00)	0.129 (1.00)	0.64 (1.00)	0.252 (1.00)	0.0446 (1.00)	0.102 (1.00)	1 (1.00)
4q gain	0.983 (1.00)	0.758 (1.00)	0.404 (1.00)	1 (1.00)	0.627 (1.00)	0.591 (1.00)	0.125 (1.00)	0.179 (1.00)	0.074 (1.00)	1 (1.00)
5p gain	0.953 (1.00)	0.356 (1.00)	0.361 (1.00)	1 (1.00)	0.192 (1.00)	0.836 (1.00)	0.232 (1.00)	0.777 (1.00)	0.517 (1.00)	0.24 (1.00)
5q gain	0.903 (1.00)	0.867 (1.00)	0.316 (1.00)	0.592 (1.00)	0.251 (1.00)	0.675 (1.00)	0.212 (1.00)	0.416 (1.00)	0.211 (1.00)	1 (1.00)
6p gain	0.624 (1.00)	0.48 (1.00)	0.0769 (1.00)	1 (1.00)	0.0508 (1.00)	0.626 (1.00)	0.0014 (1.00)	0.752 (1.00)	0.0511 (1.00)	0.107 (1.00)
6q gain	0.875 (1.00)	0.874 (1.00)	0.0866 (1.00)	0.899 (1.00)	0.0593 (1.00)	0.886 (1.00)	0.0057 (1.00)	0.631 (1.00)	0.029 (1.00)	0.307 (1.00)
7p gain	0.157 (1.00)	0.631 (1.00)	0.113 (1.00)	1 (1.00)	2.68e-05 (0.0206)	0.928 (1.00)	0.000393 (0.296)	0.0472 (1.00)	1.2e-05 (0.00925)	0.505 (1.00)
7q gain	0.216 (1.00)	0.615 (1.00)	0.16 (1.00)	0.866 (1.00)	0.000188 (0.143)	0.902 (1.00)	0.00989 (1.00)	0.0213 (1.00)	5.86e-05 (0.0447)	0.519 (1.00)
8p gain	0.64 (1.00)	0.516 (1.00)	0.551 (1.00)	0.831 (1.00)	0.911 (1.00)	0.445 (1.00)	0.323 (1.00)	0.832 (1.00)	0.89 (1.00)	1 (1.00)
8q gain	0.232 (1.00)	0.125 (1.00)	0.395 (1.00)	0.396 (1.00)	0.0428 (1.00)	0.2 (1.00)	0.256 (1.00)	0.29 (1.00)	0.367 (1.00)	0.744 (1.00)
9p gain	0.239 (1.00)	0.339 (1.00)	0.00178 (1.00)	0.717 (1.00)	0.0137 (1.00)	0.785 (1.00)	0.316 (1.00)	0.674 (1.00)	0.286 (1.00)	0.619 (1.00)
9q gain	0.106 (1.00)	0.197 (1.00)	0.0386 (1.00)	0.233 (1.00)	0.251 (1.00)	0.351 (1.00)	0.328 (1.00)	0.7 (1.00)	0.189 (1.00)	0.607 (1.00)
10p gain	0.0421 (1.00)	0.165 (1.00)	1 (1.00)	0.252 (1.00)	0.585 (1.00)	0.0906 (1.00)	0.00216 (1.00)	0.14 (1.00)	0.215 (1.00)	1 (1.00)
10q gain	0.431 (1.00)	0.0098 (1.00)	0.762 (1.00)	0.4 (1.00)	0.774 (1.00)	0.261 (1.00)	0.0237 (1.00)	0.494 (1.00)	0.122 (1.00)	1 (1.00)
11p gain	0.458 (1.00)	0.031 (1.00)	0.000607 (0.455)	0.187 (1.00)	0.000142 (0.108)	0.11 (1.00)	0.377 (1.00)	0.479 (1.00)	0.388 (1.00)	1 (1.00)
11q gain	0.414 (1.00)	0.00274 (1.00)	0.0272 (1.00)	1 (1.00)	0.0128 (1.00)	0.226 (1.00)	0.503 (1.00)	0.751 (1.00)	0.13 (1.00)	1 (1.00)
12p gain	0.659 (1.00)	0.0189 (1.00)	0.801 (1.00)	0.91 (1.00)	0.928 (1.00)	0.492 (1.00)	0.0134 (1.00)	0.409 (1.00)	0.0741 (1.00)	0.644 (1.00)
12q gain	0.834 (1.00)	0.00427 (1.00)	0.344 (1.00)	0.393 (1.00)	0.662 (1.00)	0.604 (1.00)	0.0409 (1.00)	0.443 (1.00)	0.0292 (1.00)	0.617 (1.00)
13q gain	0.29 (1.00)	0.874 (1.00)	0.0192 (1.00)	0.45 (1.00)	9.64e-09 (7.48e-06)	0.783 (1.00)	0.00128 (0.951)	0.188 (1.00)	0.00258 (1.00)	0.521 (1.00)
14q gain	0.313 (1.00)	0.908 (1.00)	0.61 (1.00)	1 (1.00)	0.707 (1.00)	0.247 (1.00)	0.445 (1.00)	0.0266 (1.00)	0.447 (1.00)	0.263 (1.00)
15q gain	0.91 (1.00)	0.405 (1.00)	1 (1.00)	0.71 (1.00)	0.137 (1.00)	0.938 (1.00)	0.741 (1.00)	0.714 (1.00)	0.715 (1.00)	1 (1.00)
16p gain	0.689 (1.00)	0.342 (1.00)	0.618 (1.00)	1 (1.00)	0.029 (1.00)	0.788 (1.00)	0.00692 (1.00)	0.371 (1.00)	0.00299 (1.00)	0.178 (1.00)
16q gain	0.853 (1.00)	0.215 (1.00)	0.454 (1.00)	1 (1.00)	0.0242 (1.00)	0.737 (1.00)	0.000453 (0.341)	0.312 (1.00)	0.000263 (0.199)	0.178 (1.00)
17p gain	0.163 (1.00)	0.669 (1.00)	0.531 (1.00)	0.78 (1.00)	0.378 (1.00)	0.65 (1.00)	0.892 (1.00)	0.00171 (1.00)	0.694 (1.00)	1 (1.00)
17q gain	0.0443 (1.00)	0.51 (1.00)	0.772 (1.00)	0.3 (1.00)	0.118 (1.00)	0.812 (1.00)	0.985 (1.00)	0.0163 (1.00)	0.371 (1.00)	1 (1.00)
18p gain	0.696 (1.00)	0.931 (1.00)	1 (1.00)	1 (1.00)	0.109 (1.00)	0.471 (1.00)	0.00124 (0.926)	1.41e-05 (0.0108)	0.718 (1.00)	0.286 (1.00)
18q gain	0.639 (1.00)	0.596 (1.00)	1 (1.00)	0.398 (1.00)	0.298 (1.00)	0.0617 (1.00)	0.0735 (1.00)	0.826 (1.00)	0.987 (1.00)	0.174 (1.00)
19p gain	0.575 (1.00)	0.37 (1.00)	0.259 (1.00)	0.884 (1.00)	0.0792 (1.00)	0.87 (1.00)	0.836 (1.00)	0.724 (1.00)	0.984 (1.00)	1 (1.00)
19q gain	0.279 (1.00)	0.351 (1.00)	0.367 (1.00)	0.499 (1.00)	0.285 (1.00)	0.892 (1.00)	0.952 (1.00)	0.992 (1.00)	0.911 (1.00)	0.608 (1.00)
20p gain	0.34 (1.00)	0.564 (1.00)	0.00146 (1.00)	0.024 (1.00)	2.61e-08 (2.02e-05)	0.35 (1.00)	0.365 (1.00)	0.0171 (1.00)	0.0352 (1.00)	1 (1.00)
20q gain	0.431 (1.00)	0.88 (1.00)	4.38e-05 (0.0335)	0.362 (1.00)	1.53e-15 (1.19e-12)	0.236 (1.00)	0.0396 (1.00)	0.0328 (1.00)	0.00142 (1.00)	0.731 (1.00)
21q gain	0.262 (1.00)	0.607 (1.00)	0.292 (1.00)	0.644 (1.00)	0.52 (1.00)	0.177 (1.00)	0.126 (1.00)	0.741 (1.00)	0.311 (1.00)	1 (1.00)
22q gain	0.487 (1.00)	0.542 (1.00)	0.0201 (1.00)	0.71 (1.00)	0.0431 (1.00)	0.722 (1.00)	0.174 (1.00)	0.179 (1.00)	0.0747 (1.00)	1 (1.00)
Xq gain	0.0508 (1.00)	0.469 (1.00)	0.583 (1.00)	0.628 (1.00)	0.69 (1.00)	0.295 (1.00)	0.128 (1.00)	1.67e-07 (0.000129)	0.0988 (1.00)	1 (1.00)
1p loss	0.054 (1.00)	0.932 (1.00)	0.774 (1.00)	0.52 (1.00)	0.231 (1.00)	0.714 (1.00)	0.0535 (1.00)	0.185 (1.00)	0.101 (1.00)	1 (1.00)
1q loss	0.717 (1.00)	0.179 (1.00)	0.651 (1.00)	0.312 (1.00)	0.76 (1.00)	0.733 (1.00)	0.192 (1.00)	0.631 (1.00)	0.365 (1.00)	0.332 (1.00)
2p loss	0.311 (1.00)	0.324 (1.00)	0.151 (1.00)	0.758 (1.00)	0.233 (1.00)	0.652 (1.00)	0.885 (1.00)	0.855 (1.00)	0.842 (1.00)	1 (1.00)
2q loss	3.78e-07 (0.000292)	0.809 (1.00)	1 (1.00)	0.73 (1.00)	0.623 (1.00)	0.644 (1.00)	0.862 (1.00)	0.658 (1.00)	0.259 (1.00)	1 (1.00)
3p loss	3.17e-05 (0.0243)	0.909 (1.00)	0.335 (1.00)	0.163 (1.00)	0.0491 (1.00)	0.116 (1.00)	0.0823 (1.00)	0.00361 (1.00)	0.0214 (1.00)	0.434 (1.00)
3q loss	0.0355 (1.00)	0.121 (1.00)	0.791 (1.00)	0.0815 (1.00)	4.51e-05 (0.0344)	0.473 (1.00)	0.835 (1.00)	0.169 (1.00)	0.152 (1.00)	0.238 (1.00)
4p loss	0.028 (1.00)	0.29 (1.00)	0.304 (1.00)	0.475 (1.00)	0.0283 (1.00)	0.765 (1.00)	0.0079 (1.00)	0.0151 (1.00)	0.00673 (1.00)	0.101 (1.00)
4q loss	0.417 (1.00)	0.288 (1.00)	0.00866 (1.00)	0.674 (1.00)	0.00174 (1.00)	0.685 (1.00)	0.08 (1.00)	0.518 (1.00)	0.153 (1.00)	0.0795 (1.00)
5p loss	0.508 (1.00)	0.66 (1.00)	0.845 (1.00)	0.481 (1.00)	0.182 (1.00)	0.649 (1.00)	0.0677 (1.00)	0.974 (1.00)	0.0829 (1.00)	0.0941 (1.00)
5q loss	0.533 (1.00)	0.659 (1.00)	0.0672 (1.00)	0.891 (1.00)	0.0666 (1.00)	0.385 (1.00)	0.0313 (1.00)	0.769 (1.00)	0.44 (1.00)	0.00908 (1.00)
6p loss	0.401 (1.00)	0.889 (1.00)	1 (1.00)	0.813 (1.00)	0.0576 (1.00)	0.871 (1.00)	0.434 (1.00)	0.605 (1.00)	0.615 (1.00)	1 (1.00)
6q loss	0.0672 (1.00)	0.211 (1.00)	0.543 (1.00)	1 (1.00)	0.0461 (1.00)	0.49 (1.00)	0.0607 (1.00)	0.000881 (0.658)	0.473 (1.00)	1 (1.00)
7q loss		0.191 (1.00)	0.0213 (1.00)	0.6 (1.00)	0.000748 (0.56)	0.84 (1.00)	0.327 (1.00)	0.924 (1.00)	0.483 (1.00)	1 (1.00)
8p loss	0.948 (1.00)	0.996 (1.00)	0.00687 (1.00)	0.0831 (1.00)	0.00258 (1.00)	0.759 (1.00)	0.0353 (1.00)	0.19 (1.00)	0.268 (1.00)	0.0493 (1.00)
8q loss	0.765 (1.00)	0.117 (1.00)	1 (1.00)	0.154 (1.00)	0.955 (1.00)	0.906 (1.00)	0.879 (1.00)	0.2 (1.00)	0.162 (1.00)	1 (1.00)
9p loss	0.0253 (1.00)	0.271 (1.00)	0.712 (1.00)	1 (1.00)	0.796 (1.00)	0.278 (1.00)	0.809 (1.00)	0.83 (1.00)	0.528 (1.00)	1 (1.00)
9q loss	0.287 (1.00)	0.00981 (1.00)	1 (1.00)	0.395 (1.00)	0.574 (1.00)	0.123 (1.00)	0.641 (1.00)	0.925 (1.00)	0.356 (1.00)	1 (1.00)
10p loss	0.566 (1.00)	0.0105 (1.00)	0.043 (1.00)	0.511 (1.00)	0.00343 (1.00)	0.552 (1.00)	0.377 (1.00)	0.595 (1.00)	0.166 (1.00)	0.48 (1.00)
10q loss	0.346 (1.00)	0.0151 (1.00)	0.00307 (1.00)	1 (1.00)	0.00126 (0.939)	0.722 (1.00)	0.77 (1.00)	0.829 (1.00)	0.537 (1.00)	0.53 (1.00)
11p loss	0.377 (1.00)	0.969 (1.00)	0.00589 (1.00)	0.743 (1.00)	0.000446 (0.336)	0.897 (1.00)	0.253 (1.00)	0.962 (1.00)	0.0439 (1.00)	0.48 (1.00)
11q loss	0.74 (1.00)	0.821 (1.00)	0.00745 (1.00)	0.378 (1.00)	0.000277 (0.21)	0.556 (1.00)	0.141 (1.00)	0.981 (1.00)	0.472 (1.00)	0.0377 (1.00)
12p loss	0.0704 (1.00)	0.986 (1.00)	0.316 (1.00)	0.0481 (1.00)	0.119 (1.00)	0.0718 (1.00)	0.0219 (1.00)	0.511 (1.00)	0.171 (1.00)	0.415 (1.00)
12q loss	0.103 (1.00)	0.578 (1.00)	1 (1.00)	0.252 (1.00)	0.266 (1.00)	0.325 (1.00)	0.032 (1.00)	0.29 (1.00)	0.139 (1.00)	1 (1.00)
13q loss	0.874 (1.00)	0.864 (1.00)	0.329 (1.00)	1 (1.00)	0.457 (1.00)	0.591 (1.00)	0.0334 (1.00)	0.426 (1.00)	0.56 (1.00)	1 (1.00)
14q loss	0.382 (1.00)	0.696 (1.00)	0.0658 (1.00)	1 (1.00)	0.000111 (0.0849)	0.476 (1.00)	0.011 (1.00)	0.219 (1.00)	0.594 (1.00)	1 (1.00)
15q loss	0.531 (1.00)	0.64 (1.00)	0.0113 (1.00)	0.851 (1.00)	0.00156 (1.00)	0.398 (1.00)	0.305 (1.00)	0.365 (1.00)	0.204 (1.00)	0.263 (1.00)
16p loss	0.993 (1.00)	0.548 (1.00)	0.379 (1.00)	0.123 (1.00)	0.522 (1.00)	0.754 (1.00)	0.592 (1.00)	0.985 (1.00)	0.817 (1.00)	1 (1.00)
16q loss	0.455 (1.00)	0.852 (1.00)	0.00999 (1.00)	0.628 (1.00)	0.0485 (1.00)	0.729 (1.00)	0.192 (1.00)	0.972 (1.00)	0.701 (1.00)	0.238 (1.00)
17p loss	0.429 (1.00)	0.587 (1.00)	0.0019 (1.00)	0.614 (1.00)	2.86e-06 (0.0022)	0.631 (1.00)	0.044 (1.00)	0.000879 (0.658)	0.019 (1.00)	0.19 (1.00)
17q loss	0.52 (1.00)	0.854 (1.00)	0.39 (1.00)	0.877 (1.00)	0.331 (1.00)	0.497 (1.00)	0.0974 (1.00)	0.922 (1.00)	0.499 (1.00)	0.152 (1.00)
18p loss	0.607 (1.00)	0.351 (1.00)	2.12e-05 (0.0163)	0.933 (1.00)	1.4e-13 (1.09e-10)	0.668 (1.00)	0.00128 (0.952)	0.00874 (1.00)	0.00152 (1.00)	0.738 (1.00)
18q loss	0.422 (1.00)	0.32 (1.00)	1.89e-07 (0.000146)	0.731 (1.00)	1.58e-13 (1.22e-10)	0.223 (1.00)	0.00037 (0.279)	0.0028 (1.00)	0.000284 (0.214)	0.494 (1.00)
19p loss	0.758 (1.00)	0.538 (1.00)	0.454 (1.00)	0.111 (1.00)	0.435 (1.00)	0.804 (1.00)	0.78 (1.00)	0.56 (1.00)	0.682 (1.00)	1 (1.00)
19q loss	0.957 (1.00)	0.166 (1.00)	0.598 (1.00)	0.235 (1.00)	0.501 (1.00)	0.895 (1.00)	0.683 (1.00)	0.404 (1.00)	0.934 (1.00)	1 (1.00)
20p loss	0.39 (1.00)	0.814 (1.00)	0.304 (1.00)	0.761 (1.00)	0.408 (1.00)	0.4 (1.00)	0.818 (1.00)	0.731 (1.00)	0.594 (1.00)	1 (1.00)
21q loss	0.35 (1.00)	0.956 (1.00)	0.00967 (1.00)	0.403 (1.00)	0.0127 (1.00)	0.641 (1.00)	0.08 (1.00)	0.843 (1.00)	0.21 (1.00)	0.695 (1.00)
22q loss	0.143 (1.00)	0.0712 (1.00)	0.17 (1.00)	0.262 (1.00)	0.0024 (1.00)	0.528 (1.00)	0.0134 (1.00)	0.243 (1.00)	0.0536 (1.00)	0.101 (1.00)
Xq loss	0.386 (1.00)	0.251 (1.00)	0.151 (1.00)	1 (1.00)	0.314 (1.00)	0.776 (1.00)	0.279 (1.00)	0.402 (1.00)	0.54 (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	15	560

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	80	495

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	60	515

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	59	516

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	36	539

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

Table S6. Get Full Table Description of clustering approach #6: '3q gain mutation analysis'

Cluster Labels	3Q GAIN MUTATED	3Q GAIN WILD-TYPE
Number of samples	53	522

'3q gain mutation analysis' versus 'Time to Death'

P value = 0.000218 (logrank test), Q value = 0.17

Table S7. Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	444	60	0.1 - 135.5 (7.0)
3Q GAIN MUTATED	38	10	0.2 - 60.0 (5.7)
3Q GAIN WILD-TYPE	406	50	0.1 - 135.5 (7.6)

Figure S1. 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 = 0.000283 (Chi-square test), Q value = 0.21

Table S8. Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	435	71	9	1	49
3Q GAIN MUTATED	32	8	3	1	8
3Q GAIN WILD-TYPE	403	63	6	0	41

Figure S2. Get High-res Image Clustering Approach #6: '3q gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

Table S9. Get Full Table Description of clustering approach #7: '4p gain mutation analysis'

Cluster Labels	4P GAIN MUTATED	4P GAIN WILD-TYPE
Number of samples	6	569

Clustering Approach #8: '4q gain mutation analysis'

Table S10. Get Full Table Description of clustering approach #8: '4q gain mutation analysis'

Cluster Labels	4Q GAIN MUTATED	4Q GAIN WILD-TYPE
Number of samples	7	568

Clustering Approach #9: '5p gain mutation analysis'

Table S11. Get Full Table Description of clustering approach #9: '5p gain mutation analysis'

Cluster Labels	5P GAIN MUTATED	5P GAIN WILD-TYPE
Number of samples	60	515

Clustering Approach #10: '5q gain mutation analysis'

Table S12. Get Full Table Description of clustering approach #10: '5q gain mutation analysis'

Cluster Labels	5Q GAIN MUTATED	5Q GAIN WILD-TYPE
Number of samples	33	542

Clustering Approach #11: '6p gain mutation analysis'

Table S13. Get Full Table Description of clustering approach #11: '6p gain mutation analysis'

Cluster Labels	6P GAIN MUTATED	6P GAIN WILD-TYPE
Number of samples	77	498

Clustering Approach #12: '6q gain mutation analysis'

Table S14. Get Full Table Description of clustering approach #12: '6q gain mutation analysis'

Cluster Labels	6Q GAIN MUTATED	6Q GAIN WILD-TYPE
Number of samples	71	504

Clustering Approach #13: '7p gain mutation analysis'

Table S15. Get Full Table Description of clustering approach #13: '7p gain mutation analysis'

Cluster Labels	7P GAIN MUTATED	7P GAIN WILD-TYPE
Number of samples	290	285

'7p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 2.68e-05 (Chi-square test), Q value = 0.021

Table S16. Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
7P GAIN MUTATED	184	15	87	2
7P GAIN WILD-TYPE	172	39	56	11

Figure S3. Get High-res Image Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'7p gain mutation analysis' versus 'TUMOR.STAGE'

P value = 1.2e-05 (Chi-square test), Q value = 0.0093

Table S17. Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	97	207	166	81
7P GAIN MUTATED	46	78	99	52
7P GAIN WILD-TYPE	51	129	67	29

Figure S4. Get High-res Image Clustering Approach #13: '7p gain mutation analysis' versus Clinical Feature #9: '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	255	320

'7q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000188 (Chi-square test), Q value = 0.14

Table S19. Clustering Approach #14: '7q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
7Q GAIN MUTATED	161	14	77	1
7Q GAIN WILD-TYPE	195	40	66	12

Figure S5. Get High-res Image Clustering Approach #14: '7q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'7q gain mutation analysis' versus 'TUMOR.STAGE'

P value = 5.86e-05 (Chi-square test), Q value = 0.045

Table S20. Clustering Approach #14: '7q gain mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	97	207	166	81
7Q GAIN MUTATED	42	67	88	46
7Q GAIN WILD-TYPE	55	140	78	35

Figure S6. Get High-res Image Clustering Approach #14: '7q gain mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

Clustering Approach #15: '8p gain mutation analysis'

Table S21. Get Full Table Description of clustering approach #15: '8p gain mutation analysis'

Cluster Labels	8P GAIN MUTATED	8P GAIN WILD-TYPE
Number of samples	108	467

Clustering Approach #16: '8q gain mutation analysis'

Table S22. Get Full Table Description of clustering approach #16: '8q gain mutation analysis'

Cluster Labels	8Q GAIN MUTATED	8Q GAIN WILD-TYPE
Number of samples	235	340

Clustering Approach #17: '9p gain mutation analysis'

Table S23. Get Full Table Description of clustering approach #17: '9p gain mutation analysis'

Cluster Labels	9P GAIN MUTATED	9P GAIN WILD-TYPE
Number of samples	79	496

Clustering Approach #18: '9q gain mutation analysis'

Table S24. Get Full Table Description of clustering approach #18: '9q gain mutation analysis'

Cluster Labels	9Q GAIN MUTATED	9Q GAIN WILD-TYPE
Number of samples	64	511

Clustering Approach #19: '10p gain mutation analysis'

Table S25. Get Full Table Description of clustering approach #19: '10p gain mutation analysis'

Cluster Labels	10P GAIN MUTATED	10P GAIN WILD-TYPE
Number of samples	28	547

Clustering Approach #20: '10q gain mutation analysis'

Table S26. Get Full Table Description of clustering approach #20: '10q gain mutation analysis'

Cluster Labels	10Q GAIN MUTATED	10Q GAIN WILD-TYPE
Number of samples	13	562

Clustering Approach #21: '11p gain mutation analysis'

Table S27. Get Full Table Description of clustering approach #21: '11p gain mutation analysis'

Cluster Labels	11P GAIN MUTATED	11P GAIN WILD-TYPE
Number of samples	39	536

'11p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000142 (Chi-square test), Q value = 0.11

Table S28. Clustering Approach #21: '11p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
11P GAIN MUTATED	18	0	21	0
11P GAIN WILD-TYPE	338	54	122	13

Figure S7. Get High-res Image Clustering Approach #21: '11p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #22: '11q gain mutation analysis'

Table S29. Get Full Table Description of clustering approach #22: '11q gain mutation analysis'

Cluster Labels	11Q GAIN MUTATED	11Q GAIN WILD-TYPE
Number of samples	39	536

Clustering Approach #23: '12p gain mutation analysis'

Table S30. Get Full Table Description of clustering approach #23: '12p gain mutation analysis'

Cluster Labels	12P GAIN MUTATED	12P GAIN WILD-TYPE
Number of samples	93	482

Clustering Approach #24: '12q gain mutation analysis'

Table S31. Get Full Table Description of clustering approach #24: '12q gain mutation analysis'

Cluster Labels	12Q GAIN MUTATED	12Q GAIN WILD-TYPE
Number of samples	78	497

Clustering Approach #25: '13q gain mutation analysis'

Table S32. Get Full Table Description of clustering approach #25: '13q gain mutation analysis'

Cluster Labels	13Q GAIN MUTATED	13Q GAIN WILD-TYPE
Number of samples	314	261

'13q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 9.64e-09 (Chi-square test), Q value = 7.5e-06

Table S33. Clustering Approach #25: '13q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
13Q GAIN MUTATED	199	12	96	2
13Q GAIN WILD-TYPE	157	42	47	11

Figure S8. Get High-res Image Clustering Approach #25: '13q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

Table S34. Get Full Table Description of clustering approach #26: '14q gain mutation analysis'

Cluster Labels	14Q GAIN MUTATED	14Q GAIN WILD-TYPE
Number of samples	19	556

Clustering Approach #27: '15q gain mutation analysis'

Table S35. Get Full Table Description of clustering approach #27: '15q gain mutation analysis'

Cluster Labels	15Q GAIN MUTATED	15Q GAIN WILD-TYPE
Number of samples	7	568

Clustering Approach #28: '16p gain mutation analysis'

Table S36. Get Full Table Description of clustering approach #28: '16p gain mutation analysis'

Cluster Labels	16P GAIN MUTATED	16P GAIN WILD-TYPE
Number of samples	96	479

Clustering Approach #29: '16q gain mutation analysis'

Table S37. Get Full Table Description of clustering approach #29: '16q gain mutation analysis'

Cluster Labels	16Q GAIN MUTATED	16Q GAIN WILD-TYPE
Number of samples	96	479

'16q gain mutation analysis' versus 'TUMOR.STAGE'

P value = 0.000263 (Chi-square test), Q value = 0.2

Table S38. Clustering Approach #29: '16q gain mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	97	207	166	81
16Q GAIN MUTATED	9	23	43	13
16Q GAIN WILD-TYPE	88	184	123	68

Figure S9. Get High-res Image Clustering Approach #29: '16q gain mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

Clustering Approach #30: '17p gain mutation analysis'

Table S39. Get Full Table Description of clustering approach #30: '17p gain mutation analysis'

Cluster Labels	17P GAIN MUTATED	17P GAIN WILD-TYPE
Number of samples	13	562

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

Table S40. Get Full Table Description of clustering approach #31: '17q gain mutation analysis'

Cluster Labels	17Q GAIN MUTATED	17Q GAIN WILD-TYPE
Number of samples	67	508

Clustering Approach #32: '18p gain mutation analysis'

Table S41. Get Full Table Description of clustering approach #32: '18p gain mutation analysis'

Cluster Labels	18P GAIN MUTATED	18P GAIN WILD-TYPE
Number of samples	21	554

'18p gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 1.41e-05 (Chi-square test), Q value = 0.011

Table S42. Clustering Approach #32: '18p gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	435	71	9	1	49
18P GAIN MUTATED	15	2	0	1	2
18P GAIN WILD-TYPE	420	69	9	0	47

Figure S10. Get High-res Image Clustering Approach #32: '18p gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

Table S43. Get Full Table Description of clustering approach #33: '18q gain mutation analysis'

Cluster Labels	18Q GAIN MUTATED	18Q GAIN WILD-TYPE
Number of samples	12	563

Clustering Approach #34: '19p gain mutation analysis'

Table S44. Get Full Table Description of clustering approach #34: '19p gain mutation analysis'

Cluster Labels	19P GAIN MUTATED	19P GAIN WILD-TYPE
Number of samples	52	523

Clustering Approach #35: '19q gain mutation analysis'

Table S45. Get Full Table Description of clustering approach #35: '19q gain mutation analysis'

Cluster Labels	19Q GAIN MUTATED	19Q GAIN WILD-TYPE
Number of samples	61	514

Clustering Approach #36: '20p gain mutation analysis'

Table S46. Get Full Table Description of clustering approach #36: '20p gain mutation analysis'

Cluster Labels	20P GAIN MUTATED	20P GAIN WILD-TYPE
Number of samples	288	287

'20p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 2.61e-08 (Chi-square test), Q value = 2e-05

Table S47. Clustering Approach #36: '20p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
20P GAIN MUTATED	176	11	93	2
20P GAIN WILD-TYPE	180	43	50	11

Figure S11. Get High-res Image Clustering Approach #36: '20p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

Table S48. Get Full Table Description of clustering approach #37: '20q gain mutation analysis'

Cluster Labels	20Q GAIN MUTATED	20Q GAIN WILD-TYPE
Number of samples	403	172

'20q gain mutation analysis' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 4.38e-05 (Fisher's exact test), Q value = 0.034

Table S49. Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	412	159
20Q GAIN MUTATED	269	131
20Q GAIN WILD-TYPE	143	28

Figure S12. Get High-res Image Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'20q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.53e-15 (Chi-square test), Q value = 1.2e-12

Table S50. Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
20Q GAIN MUTATED	251	16	125	4
20Q GAIN WILD-TYPE	105	38	18	9

Figure S13. Get High-res Image Clustering Approach #37: '20q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

Table S51. Get Full Table Description of clustering approach #38: '21q gain mutation analysis'

Cluster Labels	21Q GAIN MUTATED	21Q GAIN WILD-TYPE
Number of samples	19	556

Clustering Approach #39: '22q gain mutation analysis'

Table S52. Get Full Table Description of clustering approach #39: '22q gain mutation analysis'

Cluster Labels	22Q GAIN MUTATED	22Q GAIN WILD-TYPE
Number of samples	7	568

Clustering Approach #40: 'Xq gain mutation analysis'

Table S53. Get Full Table Description of clustering approach #40: 'Xq gain mutation analysis'

Cluster Labels	XQ GAIN MUTATED	XQ GAIN WILD-TYPE
Number of samples	17	558

'Xq gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 1.67e-07 (Chi-square test), Q value = 0.00013

Table S54. Clustering Approach #40: 'Xq gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	435	71	9	1	49
XQ GAIN MUTATED	10	4	0	1	1
XQ GAIN WILD-TYPE	425	67	9	0	48

Figure S14. Get High-res Image Clustering Approach #40: 'Xq gain mutation analysis' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

Clustering Approach #41: '1p loss mutation analysis'

Table S55. Get Full Table Description of clustering approach #41: '1p loss mutation analysis'

Cluster Labels	1P LOSS MUTATED	1P LOSS WILD-TYPE
Number of samples	68	507

Clustering Approach #42: '1q loss mutation analysis'

Table S56. Get Full Table Description of clustering approach #42: '1q loss mutation analysis'

Cluster Labels	1Q LOSS MUTATED	1Q LOSS WILD-TYPE
Number of samples	25	550

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

Table S57. Get Full Table Description of clustering approach #43: '2p loss mutation analysis'

Cluster Labels	2P LOSS MUTATED	2P LOSS WILD-TYPE
Number of samples	10	565

Clustering Approach #44: '2q loss mutation analysis'

Table S58. Get Full Table Description of clustering approach #44: '2q loss mutation analysis'

Cluster Labels	2Q LOSS MUTATED	2Q LOSS WILD-TYPE
Number of samples	8	567

'2q loss mutation analysis' versus 'Time to Death'

P value = 3.78e-07 (logrank test), Q value = 0.00029

Table S59. Clustering Approach #44: '2q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	444	60	0.1 - 135.5 (7.0)
2Q LOSS MUTATED	5	1	0.3 - 1.0 (1.0)
2Q LOSS WILD-TYPE	439	59	0.1 - 135.5 (7.3)

Figure S15. Get High-res Image Clustering Approach #44: '2q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

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

Table S60. Get Full Table Description of clustering approach #45: '3p loss mutation analysis'

Cluster Labels	3P LOSS MUTATED	3P LOSS WILD-TYPE
Number of samples	35	540

'3p loss mutation analysis' versus 'Time to Death'

P value = 3.17e-05 (logrank test), Q value = 0.024

Table S61. Clustering Approach #45: '3p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	444	60	0.1 - 135.5 (7.0)
3P LOSS MUTATED	21	5	0.3 - 14.5 (4.0)
3P LOSS WILD-TYPE	423	55	0.1 - 135.5 (7.3)

Figure S16. Get High-res Image Clustering Approach #45: '3p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #46: '3q loss mutation analysis'

Table S62. Get Full Table Description of clustering approach #46: '3q loss mutation analysis'

Cluster Labels	3Q LOSS MUTATED	3Q LOSS WILD-TYPE
Number of samples	17	558

'3q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 4.51e-05 (Chi-square test), Q value = 0.034

Table S63. Clustering Approach #46: '3q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
3Q LOSS MUTATED	13	0	1	3
3Q LOSS WILD-TYPE	343	54	142	10

Figure S17. Get High-res Image Clustering Approach #46: '3q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #47: '4p loss mutation analysis'

Table S64. Get Full Table Description of clustering approach #47: '4p loss mutation analysis'

Cluster Labels	4P LOSS MUTATED	4P LOSS WILD-TYPE
Number of samples	122	453

Clustering Approach #48: '4q loss mutation analysis'

Table S65. Get Full Table Description of clustering approach #48: '4q loss mutation analysis'

Cluster Labels	4Q LOSS MUTATED	4Q LOSS WILD-TYPE
Number of samples	113	462

Clustering Approach #49: '5p loss mutation analysis'

Table S66. Get Full Table Description of clustering approach #49: '5p loss mutation analysis'

Cluster Labels	5P LOSS MUTATED	5P LOSS WILD-TYPE
Number of samples	34	541

Clustering Approach #50: '5q loss mutation analysis'

Table S67. Get Full Table Description of clustering approach #50: '5q loss mutation analysis'

Cluster Labels	5Q LOSS MUTATED	5Q LOSS WILD-TYPE
Number of samples	60	515

Clustering Approach #51: '6p loss mutation analysis'

Table S68. Get Full Table Description of clustering approach #51: '6p loss mutation analysis'

Cluster Labels	6P LOSS MUTATED	6P LOSS WILD-TYPE
Number of samples	18	557

Clustering Approach #52: '6q loss mutation analysis'

Table S69. Get Full Table Description of clustering approach #52: '6q loss mutation analysis'

Cluster Labels	6Q LOSS MUTATED	6Q LOSS WILD-TYPE
Number of samples	33	542

Clustering Approach #53: '7q loss mutation analysis'

Table S70. Get Full Table Description of clustering approach #53: '7q loss mutation analysis'

Cluster Labels	7Q LOSS MUTATED	7Q LOSS WILD-TYPE
Number of samples	3	572

Clustering Approach #54: '8p loss mutation analysis'

Table S71. Get Full Table Description of clustering approach #54: '8p loss mutation analysis'

Cluster Labels	8P LOSS MUTATED	8P LOSS WILD-TYPE
Number of samples	145	430

Clustering Approach #55: '8q loss mutation analysis'

Table S72. Get Full Table Description of clustering approach #55: '8q loss mutation analysis'

Cluster Labels	8Q LOSS MUTATED	8Q LOSS WILD-TYPE
Number of samples	12	563

Clustering Approach #56: '9p loss mutation analysis'

Table S73. Get Full Table Description of clustering approach #56: '9p loss mutation analysis'

Cluster Labels	9P LOSS MUTATED	9P LOSS WILD-TYPE
Number of samples	39	536

Clustering Approach #57: '9q loss mutation analysis'

Table S74. Get Full Table Description of clustering approach #57: '9q loss mutation analysis'

Cluster Labels	9Q LOSS MUTATED	9Q LOSS WILD-TYPE
Number of samples	37	538

Clustering Approach #58: '10p loss mutation analysis'

Table S75. Get Full Table Description of clustering approach #58: '10p loss mutation analysis'

Cluster Labels	10P LOSS MUTATED	10P LOSS WILD-TYPE
Number of samples	40	535

Clustering Approach #59: '10q loss mutation analysis'

Table S76. Get Full Table Description of clustering approach #59: '10q loss mutation analysis'

Cluster Labels	10Q LOSS MUTATED	10Q LOSS WILD-TYPE
Number of samples	46	529

Clustering Approach #60: '11p loss mutation analysis'

Table S77. Get Full Table Description of clustering approach #60: '11p loss mutation analysis'

Cluster Labels	11P LOSS MUTATED	11P LOSS WILD-TYPE
Number of samples	40	535

Clustering Approach #61: '11q loss mutation analysis'

Table S78. Get Full Table Description of clustering approach #61: '11q loss mutation analysis'

Cluster Labels	11Q LOSS MUTATED	11Q LOSS WILD-TYPE
Number of samples	51	524

'11q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000277 (Chi-square test), Q value = 0.21

Table S79. Clustering Approach #61: '11q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
11Q LOSS MUTATED	25	2	16	5
11Q LOSS WILD-TYPE	331	52	127	8

Figure S18. Get High-res Image Clustering Approach #61: '11q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #62: '12p loss mutation analysis'

Table S80. Get Full Table Description of clustering approach #62: '12p loss mutation analysis'

Cluster Labels	12P LOSS MUTATED	12P LOSS WILD-TYPE
Number of samples	33	542

Clustering Approach #63: '12q loss mutation analysis'

Table S81. Get Full Table Description of clustering approach #63: '12q loss mutation analysis'

Cluster Labels	12Q LOSS MUTATED	12Q LOSS WILD-TYPE
Number of samples	28	547

Clustering Approach #64: '13q loss mutation analysis'

Table S82. Get Full Table Description of clustering approach #64: '13q loss mutation analysis'

Cluster Labels	13Q LOSS MUTATED	13Q LOSS WILD-TYPE
Number of samples	12	563

Clustering Approach #65: '14q loss mutation analysis'

Table S83. Get Full Table Description of clustering approach #65: '14q loss mutation analysis'

Cluster Labels	14Q LOSS MUTATED	14Q LOSS WILD-TYPE
Number of samples	143	432

'14q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000111 (Chi-square test), Q value = 0.085

Table S84. Clustering Approach #65: '14q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
14Q LOSS MUTATED	90	3	48	0
14Q LOSS WILD-TYPE	266	51	95	13

Figure S19. Get High-res Image Clustering Approach #65: '14q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #66: '15q loss mutation analysis'

Table S85. Get Full Table Description of clustering approach #66: '15q loss mutation analysis'

Cluster Labels	15Q LOSS MUTATED	15Q LOSS WILD-TYPE
Number of samples	156	419

Clustering Approach #67: '16p loss mutation analysis'

Table S86. Get Full Table Description of clustering approach #67: '16p loss mutation analysis'

Cluster Labels	16P LOSS MUTATED	16P LOSS WILD-TYPE
Number of samples	15	560

Clustering Approach #68: '16q loss mutation analysis'

Table S87. Get Full Table Description of clustering approach #68: '16q loss mutation analysis'

Cluster Labels	16Q LOSS MUTATED	16Q LOSS WILD-TYPE
Number of samples	17	558

Clustering Approach #69: '17p loss mutation analysis'

Table S88. Get Full Table Description of clustering approach #69: '17p loss mutation analysis'

Cluster Labels	17P LOSS MUTATED	17P LOSS WILD-TYPE
Number of samples	252	323

'17p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 2.86e-06 (Chi-square test), Q value = 0.0022

Table S89. Clustering Approach #69: '17p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
17P LOSS MUTATED	156	7	79	6
17P LOSS WILD-TYPE	200	47	64	7

Figure S20. Get High-res Image Clustering Approach #69: '17p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #70: '17q loss mutation analysis'

Table S90. Get Full Table Description of clustering approach #70: '17q loss mutation analysis'

Cluster Labels	17Q LOSS MUTATED	17Q LOSS WILD-TYPE
Number of samples	45	530

Clustering Approach #71: '18p loss mutation analysis'

Table S91. Get Full Table Description of clustering approach #71: '18p loss mutation analysis'

Cluster Labels	18P LOSS MUTATED	18P LOSS WILD-TYPE
Number of samples	324	251

'18p loss mutation analysis' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 2.12e-05 (Fisher's exact test), Q value = 0.016

Table S92. Clustering Approach #71: '18p loss mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	412	159
18P LOSS MUTATED	209	112
18P LOSS WILD-TYPE	203	47

Figure S21. Get High-res Image Clustering Approach #71: '18p loss mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'18p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.4e-13 (Chi-square test), Q value = 1.1e-10

Table S93. Clustering Approach #71: '18p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
18P LOSS MUTATED	201	7	107	5
18P LOSS WILD-TYPE	155	47	36	8

Figure S22. Get High-res Image Clustering Approach #71: '18p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #72: '18q loss mutation analysis'

Table S94. Get Full Table Description of clustering approach #72: '18q loss mutation analysis'

Cluster Labels	18Q LOSS MUTATED	18Q LOSS WILD-TYPE
Number of samples	352	223

'18q loss mutation analysis' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 1.89e-07 (Fisher's exact test), Q value = 0.00015

Table S95. Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	412	159
18Q LOSS MUTATED	225	124
18Q LOSS WILD-TYPE	187	35

Figure S23. Get High-res Image Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'18q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.58e-13 (Chi-square test), Q value = 1.2e-10

Table S96. Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	356	54	143	13
18Q LOSS MUTATED	214	10	114	9
18Q LOSS WILD-TYPE	142	44	29	4

Figure S24. Get High-res Image Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'18q loss mutation analysis' versus 'TUMOR.STAGE'

P value = 0.000284 (Chi-square test), Q value = 0.21

Table S97. Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	97	207	166	81
18Q LOSS MUTATED	50	112	112	62
18Q LOSS WILD-TYPE	47	95	54	19

Figure S25. Get High-res Image Clustering Approach #72: '18q loss mutation analysis' versus Clinical Feature #9: 'TUMOR.STAGE'

Clustering Approach #73: '19p loss mutation analysis'

Table S98. Get Full Table Description of clustering approach #73: '19p loss mutation analysis'

Cluster Labels	19P LOSS MUTATED	19P LOSS WILD-TYPE
Number of samples	20	555

Clustering Approach #74: '19q loss mutation analysis'

Table S99. Get Full Table Description of clustering approach #74: '19q loss mutation analysis'

Cluster Labels	19Q LOSS MUTATED	19Q LOSS WILD-TYPE
Number of samples	18	557

Clustering Approach #75: '20p loss mutation analysis'

Table S100. Get Full Table Description of clustering approach #75: '20p loss mutation analysis'

Cluster Labels	20P LOSS MUTATED	20P LOSS WILD-TYPE
Number of samples	47	528

Clustering Approach #76: '21q loss mutation analysis'

Table S101. Get Full Table Description of clustering approach #76: '21q loss mutation analysis'

Cluster Labels	21Q LOSS MUTATED	21Q LOSS WILD-TYPE
Number of samples	114	461

Clustering Approach #77: '22q loss mutation analysis'

Table S102. Get Full Table Description of clustering approach #77: '22q loss mutation analysis'

Cluster Labels	22Q LOSS MUTATED	22Q LOSS WILD-TYPE
Number of samples	122	453

Clustering Approach #78: 'Xq loss mutation analysis'

Table S103. Get Full Table Description of clustering approach #78: 'Xq loss mutation analysis'

Cluster Labels	XQ LOSS MUTATED	XQ LOSS WILD-TYPE
Number of samples	11	564

Methods & Data

Input

Cluster data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = COADREAD-TP.clin.merged.picked.txt
Number of patients = 575
Number of clustering approaches = 78
Number of selected clinical features = 10
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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