Correlation between aggregated molecular cancer subtypes and selected clinical features
Lung Adenocarcinoma (Primary solid tumor)
17 April 2019 | None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 2 different clustering approaches and 17 clinical features across 111 patients, 11 significant findings detected with P value < 0.05 and Q value < 0.25.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGIC_GRADE', 'PATHOLOGY_T_STAGE', 'PATHOLOGIC_STAGE', 'GENDER', 'COUNTRY_OF_ORIGIN', and 'ORIGIN_ASIA'.

- 3 subtypes identified in current cancer cohort by 'LINCRNA CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGIC_GRADE', 'GENDER', 'SMOKER', 'COUNTRY_OF_ORIGIN', and 'ORIGIN_ASIA'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 2 different clustering approaches and 17 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 11 significant findings detected.

Clinical Features | Statistical Tests | mRNA cHierClus subtypes | LINCRNA CHIERARCHICAL
DAYS TO DEATH OR LAST FUP | logrank test | 0.195 (0.331) | 0.239 (0.387)
HISTOLOGICAL TYPE | Fisher's exact test | 0.0597 (0.156) | 0.295 (0.436)
HISTOLOGIC GRADE | Fisher's exact test | 0.00081 (0.00688) | 0.00879 (0.046)
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) | 0.25 (0.387) | 0.105 (0.239)
PATHOLOGY T STAGE | Fisher's exact test | 0.0411 (0.127) | 0.136 (0.273)
PATHOLOGY N STAGE | Fisher's exact test | 0.777 (0.915) | 0.78 (0.915)
PATHOLOGIC STAGE | Fisher's exact test | 0.0123 (0.0521) | 0.0656 (0.159)
YEARS TO BIRTH | Kruskal-Wallis (anova) | 0.627 (0.79) | 0.177 (0.317)
ETHNICITY | Fisher's exact test | 1 (1.00) | 1 (1.00)
GENDER | Fisher's exact test | 2e-05 (0.00034) | 1e-05 (0.00034)
RACE | Fisher's exact test | 0.852 (0.966) | 0.58 (0.758)
RADIATION THERAPY | Fisher's exact test | 0.55 (0.747) | 0.371 (0.525)
BMI | Fisher's exact test | 0.162 (0.307) | 0.134 (0.273)
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) | 0.986 (1.00) | 0.945 (1.00)
SMOKER | Fisher's exact test | 0.0555 (0.156) | 0.0245 (0.0926)
COUNTRY OF ORIGIN | Fisher's exact test | 0.00947 (0.046) | 0.00014 (0.00159)
ORIGIN ASIA | Fisher's exact test | 0.0388 (0.127) | 0.00495 (0.0337)

Clustering Approach #1: 'mRNA cHierClus subtypes'

Table S1. Description of clustering approach #1: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 27 | 51

'mRNA cHierClus subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.195 (logrank test), Q value = 0.33

Table S2. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 111 | 11 | 0.2 - 35.0 (13.1)
subtype1 | 32 | 6 | 1.4 - 26.9 (13.9)
subtype2 | 25 | 3 | 0.2 - 27.4 (12.9)
subtype3 | 46 | 2 | 0.5 - 35.0 (12.2)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0597 (Fisher's exact test), Q value = 0.16

Table S3. Clustering Approach #1: 'mRNA cHierClus subtypes' 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 | 68 | 1 | 3 | 2 | 2 | 7 | 5 | 8 | 2
subtype1 | 4 | 20 | 0 | 1 | 1 | 0 | 3 | 0 | 4 | 0
subtype2 | 2 | 16 | 1 | 0 | 0 | 1 | 0 | 1 | 4 | 2
subtype3 | 7 | 32 | 0 | 2 | 1 | 1 | 4 | 4 | 0 | 0

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'HISTOLOGIC_GRADE'

P value = 0.00081 (Fisher's exact test), Q value = 0.0069

Table S4. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

nPatients | G1 | G2 | G3 | G4 | GX
--- | --- | --- | --- | --- | ---
ALL | 8 | 65 | 31 | 1 | 6
subtype1 | 1 | 23 | 7 | 0 | 2
subtype2 | 0 | 9 | 16 | 0 | 2
subtype3 | 7 | 33 | 8 | 1 | 2

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.25 (Kruskal-Wallis (anova)), Q value = 0.39

Table S5. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 70 | 81.7 (9.0)
subtype1 | 18 | 78.9 (7.6)
subtype2 | 24 | 81.7 (9.2)
subtype3 | 28 | 83.6 (9.5)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0411 (Kruskal-Wallis (anova)), Q value = 0.13

Table S6. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

| T1 | T2 | T3
--- | --- | --- | ---
ALL | 28 | 70 | 13
subtype1 | 5 | 21 | 7
subtype2 | 4 | 20 | 3
subtype3 | 19 | 29 | 3

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.777 (Kruskal-Wallis (anova)), Q value = 0.91

Table S7. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

| N0 | N1 | N2
--- | --- | --- | ---
ALL | 74 | 17 | 19
subtype1 | 24 | 3 | 6
subtype2 | 18 | 4 | 5
subtype3 | 32 | 10 | 8

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0123 (Fisher's exact test), Q value = 0.052

Table S8. Clustering Approach #1: 'mRNA cHierClus subtypes' 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 | 23 | 34 | 17 | 13 | 1 | 20 | 1
subtype1 | 0 | 4 | 12 | 4 | 5 | 1 | 7 | 0
subtype2 | 0 | 3 | 11 | 2 | 7 | 0 | 4 | 0
subtype3 | 2 | 16 | 11 | 11 | 1 | 0 | 9 | 1

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.627 (Kruskal-Wallis (anova)), Q value = 0.79

Table S9. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 111 | 62.6 (9.6)
subtype1 | 33 | 63.4 (8.4)
subtype2 | 27 | 61.8 (7.5)
subtype3 | 51 | 62.5 (11.3)

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

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

Table S10. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 3 | 35
subtype1 | 1 | 13
subtype2 | 0 | 3
subtype3 | 2 | 19

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 0.00034

Table S11. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 38 | 73
subtype1 | 3 | 30
subtype2 | 6 | 21
subtype3 | 29 | 22

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'GENDER'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.852 (Fisher's exact test), Q value = 0.97

Table S12. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 1 | 1 | 34
subtype1 | 1 | 0 | 0 | 13
subtype2 | 0 | 0 | 0 | 3
subtype3 | 0 | 1 | 1 | 18

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.55 (Fisher's exact test), Q value = 0.75

Table S13. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 72 | 22
subtype1 | 21 | 9
subtype2 | 17 | 5
subtype3 | 34 | 8

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'BMI'

P value = 0.162 (Fisher's exact test), Q value = 0.31

Table S14. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'BMI'

nPatients | NORMAL | OBESE | OVERWEIGHT | SEVERELY OBESE | UNDERWEIGHT
--- | --- | --- | --- | --- | ---
ALL | 54 | 10 | 28 | 3 | 16
subtype1 | 13 | 3 | 11 | 2 | 4
subtype2 | 14 | 1 | 4 | 0 | 8
subtype3 | 27 | 6 | 13 | 1 | 4

Figure S13. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'BMI'

'mRNA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.986 (Kruskal-Wallis (anova)), Q value = 1

Table S15. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 55 | 27.4 (23.4)
subtype1 | 20 | 28.2 (25.1)
subtype2 | 14 | 28.9 (27.5)
subtype3 | 21 | 25.7 (19.6)

Figure S14. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA cHierClus subtypes' versus 'SMOKER'

P value = 0.0555 (Fisher's exact test), Q value = 0.16

Table S16. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'SMOKER'

nPatients | NON-SMOKER | SMOKER
--- | --- | ---
ALL | 46 | 62
subtype1 | 8 | 24
subtype2 | 13 | 14
subtype3 | 25 | 24

Figure S15. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'SMOKER'

'mRNA cHierClus subtypes' versus 'COUNTRY_OF_ORIGIN'

P value = 0.00947 (Fisher's exact test), Q value = 0.046

Table S17. Clustering Approach #1: 'mRNA cHierClus subtypes' 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 | 34 | 45
subtype1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 2 | 12 | 14
subtype2 | 1 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 3 | 16
subtype3 | 0 | 1 | 12 | 1 | 1 | 1 | 0 | 0 | 19 | 15

Figure S16. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #16: 'COUNTRY_OF_ORIGIN'

'mRNA cHierClus subtypes' versus 'ORIGIN_ASIA'

P value = 0.0388 (Fisher's exact test), Q value = 0.13

Table S18. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #17: 'ORIGIN_ASIA'

nPatients | ASIAN | WESTERN
--- | --- | ---
ALL | 63 | 46
subtype1 | 15 | 17
subtype2 | 21 | 6
subtype3 | 27 | 23

Figure S17. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #17: 'ORIGIN_ASIA'

Clustering Approach #2: 'LINCRNA CHIERARCHICAL'

Table S19. Description of clustering approach #2: 'LINCRNA CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 30 | 48

'LINCRNA CHIERARCHICAL' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.239 (logrank test), Q value = 0.39

Table S20. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 111 | 11 | 0.2 - 35.0 (13.1)
subtype1 | 31 | 5 | 1.4 - 26.9 (14.1)
subtype2 | 28 | 4 | 0.2 - 27.4 (12.7)
subtype3 | 44 | 2 | 0.5 - 35.0 (12.2)

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

'LINCRNA CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.295 (Fisher's exact test), Q value = 0.44

Table S21. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' 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 | 68 | 1 | 3 | 2 | 2 | 7 | 5 | 8 | 2
subtype1 | 4 | 21 | 0 | 1 | 1 | 0 | 3 | 0 | 3 | 0
subtype2 | 3 | 18 | 1 | 0 | 0 | 1 | 0 | 1 | 4 | 2
subtype3 | 6 | 29 | 0 | 2 | 1 | 1 | 4 | 4 | 1 | 0

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

'LINCRNA CHIERARCHICAL' versus 'HISTOLOGIC_GRADE'

P value = 0.00879 (Fisher's exact test), Q value = 0.046

Table S22. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGIC_GRADE'

nPatients | G1 | G2 | G3 | G4 | GX
--- | --- | --- | --- | --- | ---
ALL | 8 | 65 | 31 | 1 | 6
subtype1 | 2 | 24 | 5 | 0 | 2
subtype2 | 0 | 12 | 16 | 0 | 2
subtype3 | 6 | 29 | 10 | 1 | 2

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

'LINCRNA CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.105 (Kruskal-Wallis (anova)), Q value = 0.24

Table S23. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 70 | 81.7 (9.0)
subtype1 | 16 | 78.8 (7.2)
subtype2 | 27 | 80.7 (9.2)
subtype3 | 27 | 84.4 (9.3)

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

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.136 (Kruskal-Wallis (anova)), Q value = 0.27

Table S24. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

| T1 | T2 | T3
--- | --- | --- | ---
ALL | 28 | 70 | 13
subtype1 | 7 | 20 | 6
subtype2 | 4 | 22 | 4
subtype3 | 17 | 28 | 3

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

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.78 (Kruskal-Wallis (anova)), Q value = 0.91

Table S25. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

| N0 | N1 | N2
--- | --- | --- | ---
ALL | 74 | 17 | 19
subtype1 | 23 | 3 | 7
subtype2 | 20 | 5 | 5
subtype3 | 31 | 9 | 7

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

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.0656 (Fisher's exact test), Q value = 0.16

Table S26. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' 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 | 23 | 34 | 17 | 13 | 1 | 20 | 1
subtype1 | 0 | 6 | 12 | 3 | 4 | 1 | 7 | 0
subtype2 | 0 | 3 | 12 | 3 | 7 | 0 | 5 | 0
subtype3 | 2 | 14 | 10 | 11 | 2 | 0 | 8 | 1

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

'LINCRNA CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.177 (Kruskal-Wallis (anova)), Q value = 0.32

Table S27. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #8: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 111 | 62.6 (9.6)
subtype1 | 33 | 64.4 (7.8)
subtype2 | 30 | 60.3 (8.4)
subtype3 | 48 | 62.7 (11.2)

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

'LINCRNA CHIERARCHICAL' versus 'ETHNICITY'

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

Table S28. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 3 | 35
subtype1 | 1 | 15
subtype2 | 0 | 3
subtype3 | 2 | 17

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

'LINCRNA CHIERARCHICAL' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.00034

Table S29. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #10: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 38 | 73
subtype1 | 5 | 28
subtype2 | 4 | 26
subtype3 | 29 | 19

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

'LINCRNA CHIERARCHICAL' versus 'RACE'

P value = 0.58 (Fisher's exact test), Q value = 0.76

Table S30. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 1 | 1 | 34
subtype1 | 1 | 0 | 1 | 14
subtype2 | 0 | 0 | 0 | 3
subtype3 | 0 | 1 | 0 | 17

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

'LINCRNA CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.371 (Fisher's exact test), Q value = 0.53

Table S31. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 72 | 22
subtype1 | 22 | 7
subtype2 | 17 | 8
subtype3 | 33 | 7

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

'LINCRNA CHIERARCHICAL' versus 'BMI'

P value = 0.134 (Fisher's exact test), Q value = 0.27

Table S32. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #13: 'BMI'

nPatients | NORMAL | OBESE | OVERWEIGHT | SEVERELY OBESE | UNDERWEIGHT
--- | --- | --- | --- | --- | ---
ALL | 54 | 10 | 28 | 3 | 16
subtype1 | 13 | 3 | 11 | 2 | 4
subtype2 | 18 | 0 | 5 | 0 | 7
subtype3 | 23 | 7 | 12 | 1 | 5

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

'LINCRNA CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.945 (Kruskal-Wallis (anova)), Q value = 1

Table S33. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #14: 'NUMBER_PACK_YEARS_SMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 55 | 27.4 (23.4)
subtype1 | 20 | 26.3 (23.2)
subtype2 | 16 | 28.6 (28.0)
subtype3 | 19 | 27.5 (20.4)

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

'LINCRNA CHIERARCHICAL' versus 'SMOKER'

P value = 0.0245 (Fisher's exact test), Q value = 0.093

Table S34. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #15: 'SMOKER'

nPatients | NON-SMOKER | SMOKER
--- | --- | ---
ALL | 46 | 62
subtype1 | 8 | 25
subtype2 | 13 | 16
subtype3 | 25 | 21

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

'LINCRNA CHIERARCHICAL' versus 'COUNTRY_OF_ORIGIN'

P value = 0.00014 (Fisher's exact test), Q value = 0.0016

Table S35. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' 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 | 34 | 45
subtype1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 2 | 14 | 13
subtype2 | 1 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 3 | 19
subtype3 | 0 | 1 | 13 | 0 | 1 | 1 | 0 | 0 | 17 | 13

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

'LINCRNA CHIERARCHICAL' versus 'ORIGIN_ASIA'

P value = 0.00495 (Fisher's exact test), Q value = 0.034

Table S36. Clustering Approach #2: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #17: 'ORIGIN_ASIA'

nPatients | ASIAN | WESTERN
--- | --- | ---
ALL | 63 | 46
subtype1 | 13 | 20
subtype2 | 24 | 6
subtype3 | 26 | 20

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

Methods & Data
Input

- Cluster data file = /cromwell_root/fc-e7058367-eaa6-44b5-aab5-1ec08acf146a/69dd18b3-97fe-4143-b0f4-32e5d55e929d/aggregate_clusters_workflow/d44c315a-761b-4b72-be1f-cb969be36102/call-aggregate_clusters/CPTAC3-LUAD-TP.mergedcluster.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 = 111

- Number of clustering approaches = 2

- Number of selected clinical features = 17

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

Clustering approaches
Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

References
[1] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[2] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[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] 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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