Correlation between APOBEC groups and selected clinical features
Lung Adenocarcinoma (Primary solid tumor)
17 April 2019  |  None
Maintainer Information
Maintained by Hailei Zhang (Broad Institute)
Overview
Introduction

This pipeline computes the correlation between APOBRC groups and selected clinical features.

Summary

Testing the association between APOBEC groups identified by 2 different apobec score and 17 clinical features across 108 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'APOBEC MUTLOAD MINESTIMATE'. These subtypes do not correlate to any clinical features.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between APOBEC groups by 2 different APOBEC scores and 17 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical
Features 		Statistical
Tests 		APOBEC
MUTLOAD
MINESTIMATE 		APOBEC
ENRICH
DAYS TO DEATH OR LAST FUP logrank test 0.926
(0.984) 		0.232
(0.838)
HISTOLOGICAL TYPE Fisher's exact test 0.399
(0.848) 		0.396
(0.848)
HISTOLOGIC GRADE Fisher's exact test 0.779
(0.966) 		0.805
(0.966)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.24
(0.838) 		0.131
(0.838)
PATHOLOGY T STAGE Fisher's exact test 0.553
(0.907) 		0.755
(0.966)
PATHOLOGY N STAGE Fisher's exact test 0.733
(0.966) 		0.321
(0.838)
PATHOLOGIC STAGE Fisher's exact test 0.51
(0.907) 		0.801
(0.966)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.286
(0.838) 		0.012
(0.409)
ETHNICITY Fisher's exact test 1
(1.00) 		1
(1.00)
GENDER Fisher's exact test 0.182
(0.838) 		0.283
(0.838)
RACE Fisher's exact test 0.691
(0.966) 		0.853
(0.966)
RADIATION THERAPY Fisher's exact test 0.56
(0.907) 		0.474
(0.907)
BMI Fisher's exact test 0.38
(0.848) 		0.55
(0.907)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.312
(0.838) 		0.224
(0.838)
SMOKER Fisher's exact test 0.877
(0.966) 		0.648
(0.966)
COUNTRY OF ORIGIN Fisher's exact test 0.142
(0.838) 		0.104
(0.838)
ORIGIN ASIA Fisher's exact test 0.881
(0.966) 		0.0583
(0.838)
APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

Table S1.  Description of APOBEC group #1: 'APOBEC MUTLOAD MINESTIMATE'

Cluster Labels 0 HIGH LOW
Number of samples 71 19 18
'APOBEC MUTLOAD MINESTIMATE' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.926 (logrank test), Q value = 0.98

Table S2.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 108 11 0.2 - 35.0 (13.0)
0 69 8 0.2 - 35.0 (13.1)
HIGH 18 2 0.5 - 27.4 (13.3)
LOW 13 1 0.2 - 26.0 (12.1)

Figure S1.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'APOBEC MUTLOAD MINESTIMATE' versus 'HISTOLOGICAL_TYPE'

P value = 0.399 (Fisher's exact test), Q value = 0.85

Table S3.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

nPatients ACINAR ADENOCARCINOMA ADENOCARCINOMA BASALOID SQUAMOUS CELL CARCINOMA INVASIVE MUCINOUS ADENOCARCINOMA LEPIDIC ADENOCARCINOMA MICROPAPILLARY ADENOCARCINOMA OTHER PAPILLARY ADENOCARCINOMA SOLID ADENOCARCINOMA SQUAMOUS CELL CARCINOMA
ALL 13 67 1 3 1 2 7 4 8 2
0 11 40 0 3 1 1 5 4 6 0
HIGH 0 14 1 0 0 1 1 0 1 1
LOW 2 13 0 0 0 0 1 0 1 1

Figure S2.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

'APOBEC MUTLOAD MINESTIMATE' versus 'HISTOLOGIC_GRADE'

P value = 0.779 (Fisher's exact test), Q value = 0.97

Table S4.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 7 63 31 1 6
0 5 41 21 1 3
HIGH 1 9 7 0 2
LOW 1 13 3 0 1

Figure S3.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

'APOBEC MUTLOAD MINESTIMATE' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.24 (Kruskal-Wallis (anova)), Q value = 0.84

Table S5.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 69 81.7 (9.1)
0 44 80.2 (8.5)
HIGH 14 85.0 (10.2)
LOW 11 83.6 (9.2)

Figure S4.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'APOBEC MUTLOAD MINESTIMATE' versus 'PATHOLOGY_T_STAGE'

P value = 0.553 (Kruskal-Wallis (anova)), Q value = 0.91

Table S6.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

T1 T2 T3
ALL 27 69 12
0 20 44 7
HIGH 3 12 4
LOW 4 13 1

Figure S5.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'APOBEC MUTLOAD MINESTIMATE' versus 'PATHOLOGY_N_STAGE'

P value = 0.733 (Kruskal-Wallis (anova)), Q value = 0.97

Table S7.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

N0 N1 N2
ALL 71 17 19
0 45 10 15
HIGH 14 3 2
LOW 12 4 2

Figure S6.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'APOBEC MUTLOAD MINESTIMATE' versus 'PATHOLOGIC_STAGE'

P value = 0.51 (Fisher's exact test), Q value = 0.91

Table S8.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #7: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IV
ALL 2 22 33 17 12 1 20 1
0 2 15 20 13 5 1 14 1
HIGH 0 3 8 0 5 0 3 0
LOW 0 4 5 4 2 0 3 0

Figure S7.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #7: 'PATHOLOGIC_STAGE'

'APOBEC MUTLOAD MINESTIMATE' versus 'YEARS_TO_BIRTH'

P value = 0.286 (Kruskal-Wallis (anova)), Q value = 0.84

Table S9.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 108 62.5 (9.7)
0 71 61.5 (10.4)
HIGH 19 63.4 (8.1)
LOW 18 65.3 (7.9)

Figure S8.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

'APOBEC MUTLOAD MINESTIMATE' versus 'ETHNICITY'

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

Table S10.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 33
0 3 22
HIGH 0 5
LOW 0 6

Figure S9.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #9: 'ETHNICITY'

'APOBEC MUTLOAD MINESTIMATE' versus 'GENDER'

P value = 0.182 (Fisher's exact test), Q value = 0.84

Table S11.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #10: 'GENDER'

nPatients FEMALE MALE
ALL 36 72
0 27 44
HIGH 3 16
LOW 6 12

Figure S10.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #10: 'GENDER'

'APOBEC MUTLOAD MINESTIMATE' versus 'RACE'

P value = 0.691 (Fisher's exact test), Q value = 0.97

Table S12.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 1 32
0 1 1 0 22
HIGH 0 0 0 5
LOW 0 0 1 5

Figure S11.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #11: 'RACE'

'APOBEC MUTLOAD MINESTIMATE' versus 'RADIATION_THERAPY'

P value = 0.56 (Fisher's exact test), Q value = 0.91

Table S13.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 69 22
0 49 13
HIGH 12 5
LOW 8 4

Figure S12.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #12: 'RADIATION_THERAPY'

'APOBEC MUTLOAD MINESTIMATE' versus 'BMI'

P value = 0.38 (Fisher's exact test), Q value = 0.85

Table S14.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #13: 'BMI'

nPatients NORMAL OBESE OVERWEIGHT SEVERELY OBESE UNDERWEIGHT
ALL 53 10 27 3 15
0 39 6 16 2 8
HIGH 5 2 7 0 5
LOW 9 2 4 1 2

Figure S13.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #13: 'BMI'

'APOBEC MUTLOAD MINESTIMATE' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.312 (Kruskal-Wallis (anova)), Q value = 0.84

Table S15.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 54 27.3 (23.6)
0 33 23.0 (19.3)
HIGH 11 39.1 (35.1)
LOW 10 28.5 (18.7)

Figure S14.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

'APOBEC MUTLOAD MINESTIMATE' versus 'SMOKER'

P value = 0.877 (Fisher's exact test), Q value = 0.97

Table S16.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #15: 'SMOKER'

nPatients NON-SMOKER SMOKER
ALL 45 60
0 31 38
HIGH 7 11
LOW 7 11

Figure S15.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #15: 'SMOKER'

'APOBEC MUTLOAD MINESTIMATE' versus 'COUNTRY_OF_ORIGIN'

P value = 0.142 (Fisher's exact test), Q value = 0.84

Table S17.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #16: 'COUNTRY_OF_ORIGIN'

nPatients BULGARIA CANADA CHINA JAMAICA MEXICO POLAND RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 2 1 18 1 2 2 1 3 32 44
0 1 1 12 0 2 0 1 0 23 30
HIGH 0 0 3 0 0 2 0 1 5 8
LOW 1 0 3 1 0 0 0 2 4 6

Figure S16.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #16: 'COUNTRY_OF_ORIGIN'

'APOBEC MUTLOAD MINESTIMATE' versus 'ORIGIN_ASIA'

P value = 0.881 (Fisher's exact test), Q value = 0.97

Table S18.  Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #17: 'ORIGIN_ASIA'

nPatients ASIAN WESTERN
ALL 62 44
0 42 28
HIGH 11 8
LOW 9 8

Figure S17.  Get High-res Image Clustering Approach #1: 'APOBEC MUTLOAD MINESTIMATE' versus Clinical Feature #17: 'ORIGIN_ASIA'

APOBEC group #2: 'APOBEC ENRICH'

Table S19.  Description of APOBEC group #2: 'APOBEC ENRICH'

Cluster Labels FC.HIGH.SIG FC.LOW.NONSIG FC.NEUTRAL
Number of samples 25 60 23
'APOBEC ENRICH' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.232 (logrank test), Q value = 0.84

Table S20.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 108 11 0.2 - 35.0 (13.0)
FC.HIGH.SIG 23 3 0.2 - 27.4 (12.4)
FC.LOW.NONSIG 58 8 0.2 - 35.0 (13.1)
FC.NEUTRAL 19 0 1.2 - 26.0 (13.3)

Figure S18.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'APOBEC ENRICH' versus 'HISTOLOGICAL_TYPE'

P value = 0.396 (Fisher's exact test), Q value = 0.85

Table S21.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

nPatients ACINAR ADENOCARCINOMA ADENOCARCINOMA BASALOID SQUAMOUS CELL CARCINOMA INVASIVE MUCINOUS ADENOCARCINOMA LEPIDIC ADENOCARCINOMA MICROPAPILLARY ADENOCARCINOMA OTHER PAPILLARY ADENOCARCINOMA SOLID ADENOCARCINOMA SQUAMOUS CELL CARCINOMA
ALL 13 67 1 3 1 2 7 4 8 2
FC.HIGH.SIG 1 18 1 0 0 1 1 0 2 1
FC.LOW.NONSIG 10 32 0 3 1 1 4 3 6 0
FC.NEUTRAL 2 17 0 0 0 0 2 1 0 1

Figure S19.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

'APOBEC ENRICH' versus 'HISTOLOGIC_GRADE'

P value = 0.805 (Fisher's exact test), Q value = 0.97

Table S22.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 7 63 31 1 6
FC.HIGH.SIG 1 14 8 0 2
FC.LOW.NONSIG 3 37 17 1 2
FC.NEUTRAL 3 12 6 0 2

Figure S20.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

'APOBEC ENRICH' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.131 (Kruskal-Wallis (anova)), Q value = 0.84

Table S23.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 69 81.7 (9.1)
FC.HIGH.SIG 20 85.0 (10.0)
FC.LOW.NONSIG 39 79.7 (8.7)
FC.NEUTRAL 10 83.0 (6.7)

Figure S21.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'APOBEC ENRICH' versus 'PATHOLOGY_T_STAGE'

P value = 0.755 (Kruskal-Wallis (anova)), Q value = 0.97

Table S24.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

T1 T2 T3
ALL 27 69 12
FC.HIGH.SIG 5 16 4
FC.LOW.NONSIG 15 38 7
FC.NEUTRAL 7 15 1

Figure S22.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'APOBEC ENRICH' versus 'PATHOLOGY_N_STAGE'

P value = 0.321 (Kruskal-Wallis (anova)), Q value = 0.84

Table S25.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

N0 N1 N2
ALL 71 17 19
FC.HIGH.SIG 17 5 3
FC.LOW.NONSIG 35 10 14
FC.NEUTRAL 19 2 2

Figure S23.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'APOBEC ENRICH' versus 'PATHOLOGIC_STAGE'

P value = 0.801 (Fisher's exact test), Q value = 0.97

Table S26.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #7: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IV
ALL 2 22 33 17 12 1 20 1
FC.HIGH.SIG 0 5 8 3 5 0 4 0
FC.LOW.NONSIG 2 10 17 12 4 1 13 1
FC.NEUTRAL 0 7 8 2 3 0 3 0

Figure S24.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #7: 'PATHOLOGIC_STAGE'

'APOBEC ENRICH' versus 'YEARS_TO_BIRTH'

P value = 0.012 (Kruskal-Wallis (anova)), Q value = 0.41

Table S27.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 108 62.5 (9.7)
FC.HIGH.SIG 25 64.6 (8.5)
FC.LOW.NONSIG 60 60.0 (10.0)
FC.NEUTRAL 23 66.7 (8.4)

Figure S25.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

'APOBEC ENRICH' versus 'ETHNICITY'

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

Table S28.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 33
FC.HIGH.SIG 0 5
FC.LOW.NONSIG 2 18
FC.NEUTRAL 1 10

Figure S26.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #9: 'ETHNICITY'

'APOBEC ENRICH' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 0.84

Table S29.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #10: 'GENDER'

nPatients FEMALE MALE
ALL 36 72
FC.HIGH.SIG 5 20
FC.LOW.NONSIG 23 37
FC.NEUTRAL 8 15

Figure S27.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #10: 'GENDER'

'APOBEC ENRICH' versus 'RACE'

P value = 0.853 (Fisher's exact test), Q value = 0.97

Table S30.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 1 32
FC.HIGH.SIG 0 0 0 5
FC.LOW.NONSIG 1 1 0 17
FC.NEUTRAL 0 0 1 10

Figure S28.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #11: 'RACE'

'APOBEC ENRICH' versus 'RADIATION_THERAPY'

P value = 0.474 (Fisher's exact test), Q value = 0.91

Table S31.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 69 22
FC.HIGH.SIG 14 7
FC.LOW.NONSIG 40 12
FC.NEUTRAL 15 3

Figure S29.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #12: 'RADIATION_THERAPY'

'APOBEC ENRICH' versus 'BMI'

P value = 0.55 (Fisher's exact test), Q value = 0.91

Table S32.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #13: 'BMI'

nPatients NORMAL OBESE OVERWEIGHT SEVERELY OBESE UNDERWEIGHT
ALL 53 10 27 3 15
FC.HIGH.SIG 9 2 9 0 5
FC.LOW.NONSIG 30 5 15 2 8
FC.NEUTRAL 14 3 3 1 2

Figure S30.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #13: 'BMI'

'APOBEC ENRICH' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.224 (Kruskal-Wallis (anova)), Q value = 0.84

Table S33.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 54 27.3 (23.6)
FC.HIGH.SIG 14 30.9 (34.0)
FC.LOW.NONSIG 28 23.1 (20.1)
FC.NEUTRAL 12 32.9 (15.2)

Figure S31.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

'APOBEC ENRICH' versus 'SMOKER'

P value = 0.648 (Fisher's exact test), Q value = 0.97

Table S34.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #15: 'SMOKER'

nPatients NON-SMOKER SMOKER
ALL 45 60
FC.HIGH.SIG 10 14
FC.LOW.NONSIG 27 31
FC.NEUTRAL 8 15

Figure S32.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #15: 'SMOKER'

'APOBEC ENRICH' versus 'COUNTRY_OF_ORIGIN'

P value = 0.104 (Fisher's exact test), Q value = 0.84

Table S35.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #16: 'COUNTRY_OF_ORIGIN'

nPatients BULGARIA CANADA CHINA JAMAICA MEXICO POLAND RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 2 1 18 1 2 2 1 3 32 44
FC.HIGH.SIG 0 0 6 0 0 2 0 1 5 11
FC.LOW.NONSIG 1 1 10 0 1 0 1 0 18 27
FC.NEUTRAL 1 0 2 1 1 0 0 2 9 6

Figure S33.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #16: 'COUNTRY_OF_ORIGIN'

'APOBEC ENRICH' versus 'ORIGIN_ASIA'

P value = 0.0583 (Fisher's exact test), Q value = 0.84

Table S36.  Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #17: 'ORIGIN_ASIA'

nPatients ASIAN WESTERN
ALL 62 44
FC.HIGH.SIG 17 8
FC.LOW.NONSIG 37 22
FC.NEUTRAL 8 14

Figure S34.  Get High-res Image Clustering Approach #2: 'APOBEC ENRICH' versus Clinical Feature #17: 'ORIGIN_ASIA'

Methods & Data
Input

  • APOBEC groups file = /cromwell_root/fc-e7058367-eaa6-44b5-aab5-1ec08acf146a/53b59b4a-b38d-4dfd-9ded-fd5171ac2ee1/mutation_apobec/ca6ef43d-ce49-4015-b418-a9b28606bbcb/call-preprocess_clinical_apobec/APOBEC_for_clinical.correlaion.input.categorical.txt

  • Clinical data file = /cromwell_root/fc-e7058367-eaa6-44b5-aab5-1ec08acf146a/39eab10a-1791-41cf-866c-13e6472ce02e/normalize_clinical_cptac/df4eac3a-77b6-4ce7-961f-e1027c1ad609/call-normalize_clinical_cptac_task_1/CPTAC3-LUAD-TP.clin.merged.picked.txt

  • Number of patients = 108

  • Number of selected clinical features = 17

APOBEC classification

APOBEC classification based on APOBEC_MutLoad_MinEstimate : a. APOBEC non group -- samples with zero value, b. APOBEC high group -- samples above median value in non zero samples, c. APOBEC low group -- samples below median value in non zero samples.

APOBEC classification based on APOBEC_enrich : a. No Enrichmment group -- all samples with BH_Fisher_p-value_tCw > 0.05, b. Low enrichment group -- samples with BH_Fisher_p-value_tCw = < 0.05 and APOBEC_enrich=<2, c. High enrichment group -- samples with BH_Fisher_p-value_tCw =< 0.05 and APOBEC_enrich>2.

Survival analysis

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

Fisher's exact test

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] 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)
[3] 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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