Correlate_Clinical_vs_Molecular_Signatures
Lung Adenocarcinoma (MOLECULAR_NONSMOKER)
07 February 2013  |  awg_luad__2013_02_07
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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C16M34ZG
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 14 clinical features across 80 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'GENDER'.

  • CNMF clustering analysis on sequencing-based miR expression data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGY.N' and 'STOPPEDSMOKINGYEAR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.

Clinical
Features
Statistical
Tests
CN
CNMF
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRseq
CNMF
subtypes
MIRseq
cHierClus
subtypes
Time to Death logrank test 0.156
(1.00)
0.247
(1.00)
0.0403
(1.00)
3.04e-07
(3.16e-05)
0.00789
(0.757)
0.744
(1.00)
0.716
(1.00)
0.192
(1.00)
AGE ANOVA 0.958
(1.00)
0.089
(1.00)
0.053
(1.00)
0.0407
(1.00)
0.739
(1.00)
0.169
(1.00)
0.531
(1.00)
0.0115
(1.00)
GENDER Fisher's exact test 0.877
(1.00)
0.551
(1.00)
0.197
(1.00)
0.0853
(1.00)
0.000198
(0.0202)
6.73e-06
(0.000693)
0.522
(1.00)
0.147
(1.00)
KARNOFSKY PERFORMANCE SCORE ANOVA
HISTOLOGICAL TYPE Chi-square test 0.617
(1.00)
0.842
(1.00)
0.135
(1.00)
0.203
(1.00)
0.398
(1.00)
0.00445
(0.44)
0.116
(1.00)
0.29
(1.00)
PATHOLOGY T Chi-square test 0.347
(1.00)
0.784
(1.00)
0.0532
(1.00)
0.011
(1.00)
0.00851
(0.809)
0.00649
(0.629)
0.74
(1.00)
0.192
(1.00)
PATHOLOGY N Chi-square test 0.984
(1.00)
0.772
(1.00)
0.175
(1.00)
0.163
(1.00)
0.0163
(1.00)
0.679
(1.00)
0.215
(1.00)
0.00071
(0.0717)
PATHOLOGICSPREAD(M) Chi-square test 0.952
(1.00)
0.048
(1.00)
0.578
(1.00)
0.945
(1.00)
0.978
(1.00)
0.984
(1.00)
0.931
(1.00)
0.873
(1.00)
TUMOR STAGE Chi-square test 0.997
(1.00)
0.815
(1.00)
0.078
(1.00)
0.154
(1.00)
0.179
(1.00)
0.976
(1.00)
0.713
(1.00)
0.00875
(0.822)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.707
(1.00)
0.192
(1.00)
0.104
(1.00)
0.452
(1.00)
0.406
(1.00)
0.582
(1.00)
0.76
(1.00)
0.569
(1.00)
NUMBERPACKYEARSSMOKED ANOVA 0.33
(1.00)
0.773
(1.00)
0.747
(1.00)
0.385
(1.00)
0.273
(1.00)
0.152
(1.00)
0.957
(1.00)
0.639
(1.00)
STOPPEDSMOKINGYEAR ANOVA 0.732
(1.00)
0.434
(1.00)
0.851
(1.00)
0.642
(1.00)
0.702
(1.00)
0.11
(1.00)
0.212
(1.00)
0.00172
(0.172)
TOBACCOSMOKINGHISTORYINDICATOR Chi-square test 0.638
(1.00)
0.993
(1.00)
0.181
(1.00)
0.249
(1.00)
0.463
(1.00)
0.235
(1.00)
0.135
(1.00)
0.0771
(1.00)
YEAROFTOBACCOSMOKINGONSET ANOVA 0.911
(1.00)
0.287
(1.00)
0.0108
(1.00)
0.115
(1.00)
0.0724
(1.00)
0.00484
(0.474)
0.157
(1.00)
0.0931
(1.00)
Clustering Approach #1: 'CN CNMF'

Table S1.  Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 21 40 19
'CN CNMF' versus 'Time to Death'

P value = 0.156 (logrank test), Q value = 1

Table S2.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 21 0.1 - 224.0 (13.2)
subtype1 18 8 0.6 - 45.3 (12.5)
subtype2 34 8 0.7 - 76.2 (10.1)
subtype3 15 5 0.1 - 224.0 (19.5)

Figure S1.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.958 (ANOVA), Q value = 1

Table S3.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 66.5 (10.6)
subtype1 18 66.4 (10.5)
subtype2 32 66.8 (10.6)
subtype3 16 65.9 (11.5)

Figure S2.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.877 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 34
subtype1 12 9
subtype2 22 18
subtype3 12 7

Figure S3.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.617 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 18 46 3 4 1 1 2 2
subtype1 2 4 12 1 0 0 0 0 2
subtype2 1 10 22 1 3 1 1 1 0
subtype3 0 4 12 1 1 0 0 1 0

Figure S4.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.347 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 21 48 10
subtype1 7 12 2
subtype2 11 21 7
subtype3 3 15 1

Figure S5.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.984 (Chi-square test), Q value = 1

Table S7.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 49 14 15
subtype1 12 4 4
subtype2 24 7 8
subtype3 13 3 3

Figure S6.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.952 (Chi-square test), Q value = 1

Table S8.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 60 4 14
subtype1 15 1 5
subtype2 31 2 6
subtype3 14 1 3

Figure S7.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.997 (Chi-square test), Q value = 1

Table S9.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 42 15 16 4
subtype1 11 4 4 1
subtype2 20 8 9 2
subtype3 11 3 3 1

Figure S8.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.707 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 75
subtype1 1 20
subtype2 2 38
subtype3 2 17

Figure S9.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.33 (ANOVA), Q value = 1

Table S11.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 40 33.0 (31.8)
subtype1 8 20.8 (18.5)
subtype2 24 33.2 (34.0)
subtype3 8 44.7 (34.4)

Figure S10.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.732 (ANOVA), Q value = 1

Table S12.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 32 1987.1 (15.0)
subtype1 7 1984.3 (18.1)
subtype2 16 1986.5 (13.9)
subtype3 9 1990.2 (15.7)

Figure S11.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.638 (Chi-square test), Q value = 1

Table S13.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 25 7 27
subtype1 2 9 1 7
subtype2 9 11 5 13
subtype3 5 5 1 7

Figure S12.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'CN CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.911 (ANOVA), Q value = 1

Table S14.  Clustering Approach #1: 'CN CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 27 1960.7 (12.1)
subtype1 7 1962.0 (8.6)
subtype2 14 1960.9 (14.3)
subtype3 6 1959.0 (11.8)

Figure S13.  Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #2: 'METHLYATION CNMF'

Table S15.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 23 20 19
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.247 (logrank test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 51 15 0.1 - 224.0 (12.7)
subtype1 20 3 0.7 - 224.0 (15.1)
subtype2 16 7 0.1 - 163.1 (13.8)
subtype3 15 5 0.8 - 48.5 (6.2)

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.089 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 48 66.6 (11.3)
subtype1 19 65.5 (10.8)
subtype2 17 63.6 (11.3)
subtype3 12 72.6 (10.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.551 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 37 25
subtype1 13 10
subtype2 14 6
subtype3 10 9

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.842 (Chi-square test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 13 33 3 4 1 1 2 2
subtype1 1 6 12 1 1 0 1 1 0
subtype2 1 4 11 1 1 0 0 0 2
subtype3 1 3 10 1 2 1 0 1 0

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.784 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 16 36 9
subtype1 4 15 4
subtype2 6 11 2
subtype3 6 10 3

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.772 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 39 11 11
subtype1 16 4 3
subtype2 13 3 3
subtype3 10 4 5

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.048 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 44 2 14
subtype1 21 1 1
subtype2 13 0 5
subtype3 10 1 8

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.815 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 32 13 12 2
subtype1 13 6 3 1
subtype2 11 3 4 0
subtype3 8 4 5 1

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.192 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 4 58
subtype1 1 22
subtype2 3 17
subtype3 0 19

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.773 (ANOVA), Q value = 1

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 29 28.1 (28.5)
subtype1 11 27.3 (32.0)
subtype2 9 33.6 (36.6)
subtype3 9 23.8 (13.2)

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.434 (ANOVA), Q value = 1

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 28 1987.2 (15.0)
subtype1 12 1986.6 (18.3)
subtype2 8 1992.6 (11.7)
subtype3 8 1982.9 (11.9)

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.993 (Chi-square test), Q value = 1

Table S27.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 13 20 3 21
subtype1 6 7 1 9
subtype2 4 6 1 6
subtype3 3 7 1 6

Figure S25.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.287 (ANOVA), Q value = 1

Table S28.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 19 1961.2 (11.8)
subtype1 9 1957.3 (11.1)
subtype2 6 1967.3 (14.1)
subtype3 4 1960.8 (7.2)

Figure S26.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S29.  Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 25 20 21
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0403 (logrank test), Q value = 1

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 57 18 0.1 - 224.0 (13.2)
subtype1 20 6 0.7 - 45.3 (6.5)
subtype2 18 4 0.1 - 224.0 (24.3)
subtype3 19 8 1.0 - 163.1 (8.4)

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.053 (ANOVA), Q value = 1

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 56 66.0 (9.8)
subtype1 21 62.9 (11.0)
subtype2 17 70.5 (7.8)
subtype3 18 65.3 (8.6)

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.197 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 38 28
subtype1 18 7
subtype2 10 10
subtype3 10 11

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.135 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 14 38 3 2 1 1 2 2
subtype1 1 4 18 0 0 1 1 0 0
subtype2 1 7 7 3 1 0 0 0 1
subtype3 1 3 13 0 1 0 0 2 1

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0532 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 16 40 9
subtype1 10 12 2
subtype2 5 13 2
subtype3 1 15 5

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.175 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 37 13 14
subtype1 15 5 3
subtype2 14 3 3
subtype3 8 5 8

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.578 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 48 3 13
subtype1 17 2 5
subtype2 13 1 5
subtype3 18 0 3

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.078 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 31 14 15 3
subtype1 13 6 2 2
subtype2 11 4 3 1
subtype3 7 4 10 0

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.104 (Fisher's exact test), Q value = 1

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 63
subtype1 3 22
subtype2 0 20
subtype3 0 21

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.747 (ANOVA), Q value = 1

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 33 34.2 (34.0)
subtype1 10 36.0 (43.2)
subtype2 16 29.8 (28.8)
subtype3 7 41.5 (34.3)

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.851 (ANOVA), Q value = 1

Table S40.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 28 1988.9 (14.0)
subtype1 10 1990.1 (13.3)
subtype2 10 1986.8 (13.8)
subtype3 8 1990.0 (16.6)

Figure S37.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.181 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 18 6 23
subtype1 5 7 0 12
subtype2 5 8 3 4
subtype3 6 3 3 7

Figure S38.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0108 (ANOVA), Q value = 1

Table S42.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 22 1960.8 (12.3)
subtype1 6 1972.2 (11.5)
subtype2 9 1954.0 (9.8)
subtype3 7 1959.7 (9.4)

Figure S39.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S43.  Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 7 11 16 10 11 11
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 3.04e-07 (logrank test), Q value = 3.2e-05

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 57 18 0.1 - 224.0 (13.2)
subtype1 3 2 0.7 - 20.5 (1.1)
subtype2 10 2 0.1 - 224.0 (18.9)
subtype3 15 5 0.7 - 76.2 (17.1)
subtype4 9 2 1.0 - 45.3 (8.0)
subtype5 9 4 0.8 - 19.5 (4.9)
subtype6 11 3 5.4 - 163.1 (22.3)

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0407 (ANOVA), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 56 66.0 (9.8)
subtype1 4 76.0 (7.9)
subtype2 9 64.1 (11.4)
subtype3 15 68.0 (7.0)
subtype4 10 58.9 (8.8)
subtype5 7 65.0 (12.5)
subtype6 11 68.2 (7.8)

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0853 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 38 28
subtype1 5 2
subtype2 5 6
subtype3 14 2
subtype4 4 6
subtype5 5 6
subtype6 5 6

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.203 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 14 38 3 2 1 1 2 2
subtype1 0 1 5 0 0 1 0 0 0
subtype2 0 3 5 1 1 0 0 0 1
subtype3 1 3 12 0 0 0 0 0 0
subtype4 1 2 6 0 0 0 1 0 0
subtype5 0 1 7 0 0 0 0 2 1
subtype6 1 4 3 2 1 0 0 0 0

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.011 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 16 40 9
subtype1 2 3 2
subtype2 4 7 0
subtype3 8 7 0
subtype4 1 5 4
subtype5 1 9 1
subtype6 0 9 2

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.163 (Chi-square test), Q value = 1

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 37 13 14
subtype1 4 2 1
subtype2 6 5 0
subtype3 12 1 2
subtype4 4 2 3
subtype5 5 1 5
subtype6 6 2 3

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.945 (Chi-square test), Q value = 1

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 48 3 13
subtype1 6 0 1
subtype2 8 0 2
subtype3 10 1 4
subtype4 8 1 1
subtype5 8 0 3
subtype6 8 1 2

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.154 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 31 14 15 3
subtype1 3 2 2 0
subtype2 5 5 0 0
subtype3 11 2 1 1
subtype4 2 3 4 1
subtype5 4 1 5 0
subtype6 6 1 3 1

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.452 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 63
subtype1 0 7
subtype2 1 10
subtype3 2 14
subtype4 0 10
subtype5 0 11
subtype6 0 11

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.385 (ANOVA), Q value = 1

Table S53.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 33 34.2 (34.0)
subtype1 3 57.3 (78.7)
subtype2 9 26.3 (18.6)
subtype3 6 24.1 (19.2)
subtype4 4 14.0 (19.3)
subtype5 3 47.5 (56.5)
subtype6 8 47.0 (29.9)

Figure S49.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.642 (ANOVA), Q value = 1

Table S54.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 28 1988.9 (14.0)
subtype1 1 1976.0 (NA)
subtype2 6 1995.2 (14.9)
subtype3 7 1983.7 (13.3)
subtype4 4 1988.0 (18.6)
subtype5 4 1986.8 (13.9)
subtype6 6 1992.8 (12.7)

Figure S50.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.249 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 18 6 23
subtype1 1 1 0 4
subtype2 5 4 1 1
subtype3 1 6 0 8
subtype4 2 3 1 4
subtype5 3 2 1 4
subtype6 4 2 3 2

Figure S51.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.115 (ANOVA), Q value = 1

Table S56.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 22 1960.8 (12.3)
subtype2 4 1962.8 (11.3)
subtype3 5 1955.6 (14.3)
subtype4 3 1975.7 (15.1)
subtype5 2 1959.0 (15.6)
subtype6 8 1957.9 (7.3)

Figure S52.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57.  Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 16 29 22 13
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00789 (logrank test), Q value = 0.76

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 21 0.1 - 224.0 (13.2)
subtype1 13 3 0.7 - 48.5 (19.5)
subtype2 25 12 0.8 - 45.3 (12.7)
subtype3 18 6 0.1 - 224.0 (16.9)
subtype4 11 0 1.1 - 43.8 (6.8)

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.739 (ANOVA), Q value = 1

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 66.5 (10.6)
subtype1 15 68.6 (10.4)
subtype2 22 64.7 (11.1)
subtype3 18 67.1 (10.0)
subtype4 11 66.3 (11.7)

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.000198 (Fisher's exact test), Q value = 0.02

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 34
subtype1 4 12
subtype2 14 15
subtype3 20 2
subtype4 8 5

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.398 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 18 46 3 4 1 1 2 2
subtype1 1 6 5 2 0 0 1 0 1
subtype2 1 4 21 0 1 0 0 1 1
subtype3 1 3 13 1 2 1 0 1 0
subtype4 0 5 7 0 1 0 0 0 0

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00851 (Chi-square test), Q value = 0.81

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 21 48 10
subtype1 2 9 5
subtype2 4 22 3
subtype3 9 13 0
subtype4 6 4 2

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0163 (Chi-square test), Q value = 1

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 49 14 15
subtype1 11 2 3
subtype2 10 9 9
subtype3 19 1 2
subtype4 9 2 1

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.978 (Chi-square test), Q value = 1

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 60 4 14
subtype1 12 1 3
subtype2 23 1 5
subtype3 17 1 3
subtype4 8 1 3

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.179 (Chi-square test), Q value = 1

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 42 15 16 4
subtype1 9 3 3 1
subtype2 9 8 10 1
subtype3 16 2 2 1
subtype4 8 2 1 1

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.406 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 75
subtype1 0 16
subtype2 2 27
subtype3 1 21
subtype4 2 11

Figure S61.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.273 (ANOVA), Q value = 1

Table S67.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 40 33.0 (31.8)
subtype1 13 47.1 (46.8)
subtype2 12 28.4 (19.5)
subtype3 7 27.0 (21.9)
subtype4 8 22.3 (17.7)

Figure S62.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.702 (ANOVA), Q value = 1

Table S68.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 32 1987.1 (15.0)
subtype1 8 1982.8 (16.0)
subtype2 10 1987.2 (17.0)
subtype3 6 1986.2 (12.2)
subtype4 8 1991.9 (14.7)

Figure S63.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.463 (Chi-square test), Q value = 1

Table S69.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 25 7 27
subtype1 4 7 2 3
subtype2 6 9 3 9
subtype3 2 5 1 12
subtype4 4 4 1 3

Figure S64.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0724 (ANOVA), Q value = 1

Table S70.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 27 1960.7 (12.1)
subtype1 8 1956.8 (11.5)
subtype2 9 1963.3 (7.8)
subtype3 4 1950.8 (14.8)
subtype4 6 1968.8 (12.4)

Figure S65.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S71.  Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2
Number of samples 18 62
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.744 (logrank test), Q value = 1

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 21 0.1 - 224.0 (13.2)
subtype1 15 5 1.0 - 76.2 (21.9)
subtype2 52 16 0.1 - 224.0 (10.5)

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.169 (t-test), Q value = 1

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 66.5 (10.6)
subtype1 17 69.2 (8.3)
subtype2 49 65.6 (11.3)

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 6.73e-06 (Fisher's exact test), Q value = 0.00069

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 34
subtype1 2 16
subtype2 44 18

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00445 (Chi-square test), Q value = 0.44

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 18 46 3 4 1 1 2 2
subtype1 1 7 5 2 0 0 1 0 2
subtype2 2 11 41 1 4 1 0 2 0

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00649 (Chi-square test), Q value = 0.63

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 21 48 10
subtype1 2 10 6
subtype2 19 38 4

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.679 (Chi-square test), Q value = 1

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 49 14 15
subtype1 12 2 4
subtype2 37 12 11

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.984 (Chi-square test), Q value = 1

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 60 4 14
subtype1 14 1 3
subtype2 46 3 11

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.976 (Chi-square test), Q value = 1

Table S79.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 42 15 16 4
subtype1 9 4 4 1
subtype2 33 11 12 3

Figure S73.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.582 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 75
subtype1 0 18
subtype2 5 57

Figure S74.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.152 (t-test), Q value = 1

Table S81.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 40 33.0 (31.8)
subtype1 14 45.5 (45.5)
subtype2 26 26.3 (19.2)

Figure S75.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.11 (t-test), Q value = 1

Table S82.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 32 1987.1 (15.0)
subtype1 10 1980.2 (16.1)
subtype2 22 1990.2 (13.8)

Figure S76.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.235 (Chi-square test), Q value = 1

Table S83.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 25 7 27
subtype1 5 8 1 3
subtype2 11 17 6 24

Figure S77.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.00484 (t-test), Q value = 0.47

Table S84.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 27 1960.7 (12.1)
subtype1 8 1950.6 (10.1)
subtype2 19 1965.0 (10.4)

Figure S78.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S85.  Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 16 20 12 21 11
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.716 (logrank test), Q value = 1

Table S86.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 21 0.1 - 224.0 (13.2)
subtype1 13 4 0.6 - 71.0 (15.8)
subtype2 15 5 1.1 - 46.7 (12.7)
subtype3 12 2 0.7 - 76.2 (17.5)
subtype4 16 7 0.1 - 224.0 (7.3)
subtype5 11 3 0.8 - 44.4 (19.5)

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.531 (ANOVA), Q value = 1

Table S87.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 66.5 (10.6)
subtype1 12 65.6 (10.9)
subtype2 18 69.7 (11.7)
subtype3 12 67.4 (7.6)
subtype4 14 63.4 (7.6)
subtype5 10 65.1 (14.7)

Figure S80.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.522 (Chi-square test), Q value = 1

Table S88.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 34
subtype1 10 6
subtype2 10 10
subtype3 6 6
subtype4 15 6
subtype5 5 6

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.116 (Chi-square test), Q value = 1

Table S89.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 18 46 3 4 1 1 2 2
subtype1 0 2 12 0 2 0 0 0 0
subtype2 2 8 10 0 0 0 0 0 0
subtype3 1 4 5 1 0 0 1 0 0
subtype4 0 0 15 1 1 1 0 1 2
subtype5 0 4 4 1 1 0 0 1 0

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.74 (Chi-square test), Q value = 1

Table S90.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 21 48 10
subtype1 4 10 2
subtype2 3 15 2
subtype3 5 4 2
subtype4 6 13 2
subtype5 3 6 2

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.215 (Chi-square test), Q value = 1

Table S91.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 49 14 15
subtype1 11 1 4
subtype2 13 3 3
subtype3 10 0 1
subtype4 10 7 4
subtype5 5 3 3

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.931 (Chi-square test), Q value = 1

Table S92.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 60 4 14
subtype1 13 1 2
subtype2 16 1 3
subtype3 8 1 2
subtype4 16 0 4
subtype5 7 1 3

Figure S85.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.713 (Chi-square test), Q value = 1

Table S93.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 42 15 16 4
subtype1 9 2 4 1
subtype2 12 4 3 1
subtype3 9 1 1 1
subtype4 7 6 6 0
subtype5 5 2 2 1

Figure S86.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.76 (Chi-square test), Q value = 1

Table S94.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 75
subtype1 0 16
subtype2 2 18
subtype3 1 11
subtype4 1 20
subtype5 1 10

Figure S87.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.957 (ANOVA), Q value = 1

Table S95.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 40 33.0 (31.8)
subtype1 7 39.1 (13.5)
subtype2 10 29.0 (44.2)
subtype3 8 33.8 (36.3)
subtype4 8 28.4 (19.4)
subtype5 7 37.1 (37.2)

Figure S88.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.212 (ANOVA), Q value = 1

Table S96.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 32 1987.1 (15.0)
subtype1 3 1979.0 (10.4)
subtype2 7 1980.4 (20.4)
subtype3 7 1982.7 (11.8)
subtype4 8 1994.1 (12.6)
subtype5 7 1993.4 (13.1)

Figure S89.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.135 (Chi-square test), Q value = 1

Table S97.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 25 7 27
subtype1 0 6 3 7
subtype2 3 7 1 8
subtype3 2 5 1 3
subtype4 5 5 1 8
subtype5 6 2 1 1

Figure S90.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.157 (ANOVA), Q value = 1

Table S98.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 27 1960.7 (12.1)
subtype1 4 1962.2 (11.3)
subtype2 6 1958.7 (8.9)
subtype3 6 1952.3 (11.7)
subtype4 5 1961.2 (8.3)
subtype5 6 1969.8 (14.9)

Figure S91.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S99.  Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 6 37 37
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.192 (logrank test), Q value = 1

Table S100.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 21 0.1 - 224.0 (13.2)
subtype1 5 2 5.1 - 21.9 (12.7)
subtype2 32 8 0.6 - 76.2 (18.9)
subtype3 30 11 0.1 - 224.0 (10.4)

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0115 (ANOVA), Q value = 1

Table S101.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 66.5 (10.6)
subtype1 6 60.0 (10.6)
subtype2 34 70.1 (9.8)
subtype3 26 63.3 (10.3)

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.147 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 34
subtype1 1 5
subtype2 23 14
subtype3 22 15

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.29 (Chi-square test), Q value = 1

Table S103.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 18 46 3 4 1 1 2 2
subtype1 1 1 3 0 0 0 0 0 1
subtype2 1 12 20 2 1 0 1 0 0
subtype3 1 5 23 1 3 1 0 2 1

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.192 (Chi-square test), Q value = 1

Table S104.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 21 48 10
subtype1 0 4 2
subtype2 10 24 2
subtype3 11 20 6

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00071 (Chi-square test), Q value = 0.072

Table S105.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 49 14 15
subtype1 1 4 1
subtype2 29 1 5
subtype3 19 9 9

Figure S97.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.873 (Chi-square test), Q value = 1

Table S106.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 60 4 14
subtype1 5 0 1
subtype2 29 2 5
subtype3 26 2 8

Figure S98.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00875 (Chi-square test), Q value = 0.82

Table S107.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 42 15 16 4
subtype1 1 4 1 0
subtype2 27 3 5 2
subtype3 14 8 10 2

Figure S99.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.569 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 75
subtype1 1 5
subtype2 2 35
subtype3 2 35

Figure S100.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.639 (ANOVA), Q value = 1

Table S109.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 40 33.0 (31.8)
subtype1 3 17.3 (17.2)
subtype2 21 36.0 (36.5)
subtype3 16 32.0 (27.5)

Figure S101.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.00172 (ANOVA), Q value = 0.17

Table S110.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 32 1987.1 (15.0)
subtype1 2 2005.5 (7.8)
subtype2 14 1977.5 (10.6)
subtype3 16 1993.1 (14.0)

Figure S102.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0771 (Chi-square test), Q value = 1

Table S111.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 16 25 7 27
subtype1 2 2 0 2
subtype2 2 15 5 13
subtype3 12 8 2 12

Figure S103.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0931 (ANOVA), Q value = 1

Table S112.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 27 1960.7 (12.1)
subtype1 2 1966.5 (4.9)
subtype2 14 1956.5 (11.2)
subtype3 11 1965.1 (12.8)

Figure S104.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data
Input
  • Cluster data file = MOLECULAR_NONSMOKER.mergedcluster.txt

  • Clinical data file = MOLECULAR_NONSMOKER.clin.merged.picked.txt

  • Number of patients = 80

  • Number of clustering approaches = 8

  • Number of selected clinical features = 14

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

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

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] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[8] 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)