Median (minimum-maximum)Total number of mapped bases 2,504,424,995 (718,875,018-6,059,712,422)Read depth of the target regiona Mean: 49.8 (13.9-117.3)Number of target sites with both normal andtumor depths ≥ 10 27,516,112 (18,965,079-29,556,816)

Number of tumor-specific substitutions andsmall indels in the target 47 (1-1,059)

Supplementary Table S1. Sequenced reads per tumor and normal sample.

aThe target region indicates the genomic region of the exome-capturing libraries.

θT (MuTect parameter) Number of detectedgenes

Number of detectedpotential true positives

(%)

Number of detectedpotential false positives

(%)

Default: θT = 6.3 139 8 (6%) 5 (4%)θT = 5.5 183 8 (4%) 8 (4%)θT = 7.4 59 8 (14%) 2 (3%)

Our pipeline: θT = 8.0 48 6 (13%) 2 (4%)

Comparison of significantly mutated genes detected using various MuTect thresholds."Default" used the recommended default threshold (Cibulskis et al . 2013), while "Ourpipeline" used a more stringent threshold (see Supplementary Methods). Potential truepositives include previously reported driver genes (45 genes included in SupplementaryTable S6). Potential false positives include olfactory receptor genes and other known falsepositives (mucins, CSMD1 , CSMD3 , TTN , RYR2 , and RYR3 ).

Supplementary Table S2. Comparison of significantly mutated genes detected usingvarious MuTect thresholds.

Substitution typesTotal number of tumor-specific substitutions

Per Mbp Substitution typesTotal number of tumor-specific substitutions

Per Mbp

A→C/T→G 213 0.1 A→C/T→G 538 0.3A→G/T→C 645 0.4 A→G/T→C 1,857 1.1A→T/T→A 300 0.2 A→T/T→A 1,592 0.9

C→A/G→T (non-CpG) 1,302 0.9 C→A/G→T (non-CpG) 5,629 3.7C→G/G→C (non-CpG) 514 0.3 C→G/G→C (non-CpG) 2,240 1.5C→T/G→A (non-CpG) 1,149 0.8 C→T/G→A (non-CpG) 3,612 2.4

C→A/G→T (CpG) 1,117 6.5 C→A/G→T (CpG) 1,643 9.2C→G/G→C (CpG) 45 0.3 C→G/G→C (CpG) 309 1.7C→T/G→A (CpG) 915 5.3 C→T/G→A (CpG) 1,445 8.1

Statistics are shown for the nine substitution types that were considered. The substitution types are strand-collapsed. For instance, A→C indicates A to C substitutions on either the top or bottom of the chromosome strand.

Supplementary Table S3. Comparison of the number and rate of substitutions between never-smokers and smokers.

Never-smokers Smokers

Factors consideredfor BMR calculation

Minimum number of SN sitesrequired for local BMR

calculation (ΣNSN)

Number ofdetected genes

Number ofdetected potentialtrue positives (%)

Number ofdetected potentialfalse positives (%)

From previous approaches Base type N/A 90 9 (10%) 9 (10%)

Our approach, min. ΣNSN=500Base type,local BMR,

site degeneracy500 74 6 (8%) 1 (1%)

Our approach, min. ΣNSN=3000Base type,local BMR,

site degeneracy3000 48 6 (13%) 2 (4%)

Our approach, min. ΣNSN=5000Base type,local BMR,

site degeneracy5000 36 6 (17%) 2 (6%)

Supplementary Table S4. Comparison of various methods used to compute the background mutation rate (BMR).

Comparison of various methods used to compute the background mutation rate (BMR). Previous studies (Ding et al .2008, Kan et al . 2010) calculated the BMR after taking into account base type, but not local BMR variations. On the otherhand, we took both into account and also considered site degeneracy when computing BMR (see SupplementaryMethods). We used various parameters (minimum number of synonymous [SN] sites = ΣNSN) in the local BMRcalculations and selected the best performing option: ΣNSN = 3000. Potential true positives include previously reporteddriver genes (45 genes included in Supplementary Table S6). Potential false positives include olfactory receptor genes andother known false positives (mucins, CSMD1 , CSMD3 , TTN , RYR2 , and RYR3 ).

Gene symbol RefSeq ID q value Prevalence Number of affectedpatients

Strand ORF length Annotation

EGFR NM_005228 <2.2×10-16 42.4% 72 + 3,633 41:(3n)D, 41:N, 1:S, 2:fsDTTN NM_133378 <2.2×10-16 25.3% 43 - 100,272 74:N, 12:S, 7:fsD, 4:*, 2:fsI, 1:F, 1:TTP53 NM_000546 <2.2×10-16 25.3% 43 - 1,182 31:N, 8:*, 2:S, 2:fsD, 1:T, 1:fsIKRAS NM_033360 3.9×10-9 11.8% 20 - 570 21:NPIK3CA NM_006218 5.4×10-8 8.2% 14 + 3,207 15:N, 2:SDNAH11 NM_003777 5.8×10-8 6.5% 11 + 13,572 11:N, 1:fsILPHN3 NM_015236 2.8×10-7 5.9% 10 + 4,410 11:NPCDH15 NM_001142763 4.6×10-7 8.8% 15 - 5,889 17:N, 2:S, 1:fpI, 1:*PCLO NM_033026 9.4×10-7 4.7% 8 - 15,429 8:N, 2:*, 1:S, 1:fsICACNA1E NM_001205293 4.7×10-6 4.1% 7 + 6,942 9:N, 1:fsIMYH8 NM_002472 6.5×10-6 4.1% 7 - 5,814 7:N, 2:*, 2:SHYDIN NM_032821 6.8×10-6 7.1% 12 - 15,363 13:N, 2:S, 2:T, 1:FACACB NM_001093 9.7×10-6 5.3% 9 + 7,377 9:NCOL6A3 NM_004369 2.3×10-5 4.1% 7 - 9,534 7:N, 2:*PXDNL NM_144651 4.4×10-5 4.1% 7 - 4,392 8:N, 1:*, 1:SUSH2A NM_206933 4.9×10-5 12.4% 21 - 15,609 26:N, 2:SFRAS1 NM_025074 8.6×10-5 5.9% 10 + 12,039 9:N, 1:S, 1:T, 1:fsIPRRC2C NM_015172 1.7×10-4 2.9% 5 + 8,454 5:N, 1:*, 1:S, 1:T, 1:fsDSLCO1B7 NM_001009562 2.2×10-4 4.7% 8 + 1,923 6:N, 1:*, 1:fsDLAMA2 NM_000426 2.7×10-4 3.5% 6 + 9,369 7:N, 1:*, 1:FSETD2 NM_014159 1.1×10-3 5.3% 9 - 7,695 4:N, 4:fsD, 1:*, 1:S, 1:fsICNTNAP4 NM_138994 1.3×10-3 4.1% 7 + 3,708 7:N, 1:*, 1:S, 1:fsDPDE3A NM_000921 1.3×10-3 3.5% 6 + 3,426 7:NPDE4DIP NM_001198834 1.3×10-3 4.7% 8 - 7,089 8:N, 1:*, 1:SASXL3 NM_030632 3.3×10-3 2.9% 5 + 6,747 6:N, 1:SCOL22A1 NM_152888 5.1×10-3 3.5% 6 - 4,881 7:N, 1:S, 1:fsDFAT3 NM_001008781 5.4×10-3 7.6% 13 + 13,674 15:N, 2:S, 1:fsISYNE1 NM_182961 5.4×10-3 6.5% 11 - 26,394 14:N, 2:SASPM NM_018136 6.2×10-3 4.1% 7 - 10,434 5:N, 1:*, 1:FCENPF NM_016343 7.0×10-3 4.7% 8 + 9,345 7:N, 1:fsIPKHD1L1 NM_177531 7.5×10-3 8.2% 14 + 12,732 13:N, 3:S, 1:fpD, 1:T, 1:fsDHMCN1 NM_031935 9.8×10-3 5.9% 10 + 16,908 9:N, 2:S, 1:fsDFAM214A NM_019600 1.0×10-2 1.2% 2 - 3,231 6:NPTPRZ1 NM_002851 1.6×10-2 3.5% 6 + 6,948 5:N, 3:S, 1:fsDCOL11A1 NM_080629 1.6×10-2 5.3% 9 - 5,457 5:N, 5:S, 3:F, 2:T, 1:fsIEGF NM_001963 1.9×10-2 2.4% 4 + 3,624 5:N, 1:*FAM135B NM_015912 2.2×10-2 4.1% 7 - 4,221 6:N, 2:*, 1:SCSMD3 NM_198123 3.0×10-2 12.4% 21 - 11,124 25:N, 7:S, 2:*, 2:fsD, 1:fsIPTPRT NM_133170 3.0×10-2 3.5% 6 - 4,383 4:N, 2:*C1orf173 NM_001002912 3.4×10-2 4.7% 8 - 4,593 8:N, 3:SRB1 NM_000321 3.6×10-2 5.9% 10 + 2,787 3:*, 3:T, 2:N, 2:fsD, 1:SCOPA NM_001098398 4.4×10-2 4.1% 7 - 3,702 5:N, 1:F, 1:fsDLRBA NM_006726 4.4×10-2 3.5% 6 - 8,592 4:N, 2:*F8 NM_000132 4.4×10-2 2.9% 5 - 7,056 6:NSLIT2 NM_004787 4.4×10-2 2.9% 5 + 4,590 5:N, 1:*GCN1L1 NM_006836 4.5×10-2 2.9% 5 - 8,016 5:NMAP1B NM_005909 4.5×10-2 2.9% 5 + 7,407 5:NCASP8AP2 NM_012115 4.5×10-2 2.4% 4 + 5,901 5:N, 1:S

Forty-eight genes passed the Poisson tests after correcting for multiple tests. Genes are sorted by q value. Thetop (+) or bottom (-) strand on which the reference cDNA is transcribed is indicated. “ORF length” indicates theORF length (bp) of the reference cDNA. The number of mutations followed by the mutation type discovered in thegene is shown. The listed mutation types include: S, synonymous; N, nonsynonymous; F, 5' splice-site; T, 3' splice-site; *, stop gain; fp, frame-preserving indel; fs, frame-shifting indel; D, deletion; I, insertion. Otherwise, as in Table2.

Supplemental Table S5. Significantly mutated genes detected in our discovery cohort.

Previousstudiesa

Genes Genes Prevalence Genes Prevalence Genes Prevalence Genes PrevalenceAPC EGFR 42.4% EGFR 42.4% SYNE1 6.5% LRP1B 9.4%

ARID1A TP53 25.3% TP53 25.3% FRAS1 5.9% APC 3.5%ATM KRAS 11.8% KRAS 11.8% HMCN1 5.9% NF1 3.5%

BRAF PIK3CA 8.2% PIK3CA 8.2% ACACB 5.3% PTPRD 3.5%BRD3 ACACB 5.3% RB1 5.9% PDE4DIP 5.3% BRAF 2.9%CBL COL6A3 4.1% SETD2 5.3% COL11A1 5.3% EPHA5 2.9%

CDKN2A FRAS1 5.9% CENPF 4.7% GNAS 2.9%CTNNB1 PRRC2C 2.9% COL6A3 4.1% SMAD4 2.9%

EGFR LAMA2 3.5% COPA 4.1% PTEN 2.4%EPHA3 PDE4DIP 5.3% LAMA2 3.5% CTNNB1 1.8%EPHA5 SETD2 5.3% LRBA 3.5% ERBB4 1.8%ERBB4 SYNE1 6.5% PRRC2C 2.9% PDGFRA 1.8%FBXW7 CENPF 4.7% F8 2.9% RBM10 1.8%FGFR3 HMCN1 5.9% SLIT2 2.9% ARID1A 1.2%FGFR4 COL11A1 5.3% GCN1L1 2.9% ATM 1.2%GNAS RB1 5.9% MAP1B 2.9% CBL 1.2%GOPC COPA 4.1% EPHA3 1.2%INHBA LRBA 3.5% INHBA 1.2%KDR F8 2.9% NTRK3 1.2%

KEAP11 SLIT2 2.9% PAK3 1.2%KIAA0427 GCN1L1 2.9% PPP2R1A 1.2%

KRAS MAP1B 2.9% SMARCA4 1.2%LRP1B CDKN2A 0.6%

LTK FBXW7 0.6%MET GOPC 0.6%NF1 KDR 0.6%

NRAS LTK 0.6%NTRK1 MET 0.6%NTRK3 STK11 0.6%PAK3

PDGFRAPIK3CA

PPP2R1APTEN

PTPRDRB1

RBM10SETD2

SLC38A3SMAD4

SMARCA4STK11TP53

USAF1ZMYND10

Supplementary Table S6. Comparison of significantly mutated genes and previously reported cancer driver genes.

aPotential cancer driver genes reported in previous studies (see Supplementary Table S15).bSignificantly mutated genes discovered in our study.cGenes detected as significantly mutated from in studies.dNovel potential cancer driver genes that were only found in our study.ePreviously reported cancer driver genes that demonstrated ≥ 1 detected mutation in this study but whose level ofsignificance did not pass our cutoff value.

Our studyb Found in bothc Novel candidatesd Driver candidates with ≥1detected mutation(s)e

CNV region Chromosomallocation

Amplification orDeletion GISTIC q value Genes located in CNV region

chr5:1-6556704 5p15.33 Amplification 4.2×10-3BRD9, C5orf38, CCDC127, CLPTM1L, EXOC3, IRX2,

KIAA0947, LPCAT1, MED10, MRPL36, NDUFS6,NKD2, PDCD6, SDHA, SLC12A7, TERT, TPPP, TRIP13

chr6:116571764-116575171 6q22.1 Deletion 4.6×10-3 TSPYL4

chr8:124985159-132097117 8q24.21 Amplification 4.2×10-3ASAP1, FAM49B, FAM84B, KIAA0196, MTSS1, MYC,

NDUFB9, NSMCE2, RNF139, SQLE, TATDN1,TMEM65, TRIB1, TRMT12

chr9:21465103-23366333 9p21.3 Deletion 3.1×10-8 CDKN2A, CDKN2B, MTAPchr11:107230625-107259593 11q22.3 Amplification 5.8×10-3 CWF19L2

chr12:68940295-69917929 12q15 Amplification 4.4×10-3 CPM, CPSF6, LYZ, MDM2, NUP107, RAP1B,SLC35E3, YEATS4

chr13:48944113-48944214 13q14.2 Deletion 5.9×10-3 RB1

chr14:36307656-38533094 14q13.3 Amplification 4.2×10-3 BRMS1L, FOXA1, MBIP, NKX2-1, SFTA3

chr17:37877330-37919507 17q12 Amplification 4.4×10-3 ERBB2, GRB7chr18:46778048-46778698 18q21.1 Deletion 4.6×10-3 DYMchr20:52194786-54489898 20q13.2 Amplification 4.2×10-3 BCAS1, CYP24A1, PFDN4, ZNF217chr22:45135807-45143621 22q13.31 Amplification 4.2×10-3 PRR5-ARHGAP8chr22:50952958-50953149 22q13.33 Deletion 2.0×10-3 NCAPH2

Supplementary Table S7. Thirteen significantly altered CNV regions detected in our discovery cohort.CNV regions are sorted by the q values determined using GISTIC (see Supplementary Methods). The representativegene of each CNV region is shown in bold.

Gene symbol Location Type Drugs

COL11A1 a Extracellular space Collagen Collagenase clostridium hystolyticumCOL6A3 a Extracellular space Collagen Collagenase clostridium hystolyticumEGFR b Plasma membrane Kinase Erlotinib/gemcitabineERBB2 c Plasma membrane Kinase TrastuzumabF8 d Extracellular space Peptidase Drotrecogin alfaPIK3CA e Cytoplasm Kinase NVP-BEZ235SQLE f Cytoplasm Enzyme TerbinafineTERT g Nucleus Enzyme GRN163L

Four oncogenes (EGFR , ERBB2 , PIK3CA , and TERT ) are known targets of available drugs. Proteins expressedby two collagen genes (COL11A1 and COL6A3 ) could be degraded by collagenases. Coagulation factor F8could be inhibited by drotrecogin alfa. SQLE (squalene monooxygenase) could be inhibited by terbinafine.

Supplementary Table S8. Druggable target candidates in the 22 significantly mutated genes and the 60 genes inthe significantly altered CNV regions detected in the discovery cohort.

aMandl et al ., J. Clin. Invest., 1953, 32:1323-9.bByrne and Garst, Curr. Oncol. Rep., 2005, 7:241-7.cStephens et al ., Nature, 2004, 431:525-6.dRanieri et al ., N. Engl. J. Med., 2012, 366:2055-64.eEngelman et al ., Nat. Med., 2008, 14:1351-6.fNowosielski et al ., J. Chem. Inf. Model., 2011, 51:455-62.gAsai et al ., Cancer Res., 2003, 63:3931-9.

Pathway Referencedatabase P value Benjamini-Hochberg

adjusted P value Affected genes

Bladder cancer KEGG_PATHWAY 6.7×10-10 5.0×10-8 MDM2, CDKN2A, EGFR, RB1, TP53, KRAS, ERBB2, MYC

Non-small cell lung cancer KEGG_PATHWAY 1.6×10-7 6.1×10-6 CDKN2A, EGFR, PIK3CA, RB1, TP53, KRAS, ERBB2

YAWHTAP_GGEKamoilG 4.2×10-7 1.0×10-5 MDM2, CDKN2A, EGFR, PIK3CA, RB1, TP53, KRAS

Melanoma KEGG_PATHWAY 8.6×10-7 1.6×10-5 MDM2, CDKN2A, EGFR, PIK3CA, RB1, TP53, KRAS

Pancreatic cancer KEGG_PATHWAY 9.3×10-7 1.4×10-5 CDKN2A, EGFR, PIK3CA, RB1, TP53, KRAS, ERBB2

Chronic myeloid leukemia KEGG_PATHWAY 1.2×10-6 1.5×10-5 MDM2, CDKN2A, PIK3CA, RB1, TP53, KRAS, MYC

Pathways in cancer KEGG_PATHWAY 1.5×10-6 1.6×10-5MDM2, CDKN2A, CDKN2B, EGFR,

LAMA2, PIK3CA, RB1, TP53, KRAS, ERBB2, MYC

Prostate cancer KEGG_PATHWAY 3.3×10-6 3.0×10-5 MDM2, EGFR, PIK3CA, RB1, TP53, KRAS, ERBB2

Endometrial cancer KEGG_PATHWAY 4.2×10-6 3.4×10-5 EGFR, PIK3CA, TP53, KRAS, ERBB2, MYC

Telomeres, Telomerase, Cellular Aging, and Immortality

BIOCARTA 1.6×10-5 1.0×10-3 EGFR, RB1, TERT, TP53, KRAS

Small cell lung cancer KEGG_PATHWAY 4.5×10-5 3.3×10-4 CDKN2B, LAMA2, PIK3CA, RB1, TP53, MYC

Tumor Suppressor Arf Inhibits Ribosomal Biogenesis BIOCARTA 2.0×10-4 6.3×10-3 MDM2, CDKN2A, RB1, TP53

Cell cycle KEGG_PATHWAY 3.0×10-4 2.0×10-3 MDM2, CDKN2A, CDKN2B, RB1, TP53, MYC

Focal adhesion KEGG_PATHWAY 3.3×10-4 2.1×10-3 RAP1B, COL6A3, COL11A1, EGFR, LAMA2, PIK3CA, ERBB2

Colorectal cancer KEGG_PATHWAY 6.7×10-4 3.8×10-3 EGFR, PIK3CA, TP53, KRAS, MYC

ErbB signaling pathway KEGG_PATHWAY 7.7×10-4 4.1×10-3 EGFR, PIK3CA, KRAS, ERBB2, MYC

ATRACOIBelcyC lleC 2.3×10-3 4.8×10-2 CDKN2A, CDKN2B, RB1, TP53E2F Transcriptional Activity Cell Cycle BBID 3.2×10-3 3.5×10-2 CDKN2A, RB1, TP53

Thyroid cancer KEGG_PATHWAY 8.7×10-3 4.2×10-2 TP53, KRAS, MYC

Supplementary Table S9. Pathways significantly enriched in the 22 significantly mutated genes and the 60 genes included in the significantly altered CNV regions that were detected in the discovery cohort.

Supplementary Table S10.Cox multivariable regression analysis for RB pathway alterations.

Parametera HR 95% CI P HR 95% CI P

(trohoc yrevocsiD n ()031 = n = 27)429.0)001> ot 0(0969.0)42.2 ot 64.0(20.1elam susrev elameF

Age, ≥60 versus <60 0.87 (0.49 to 1.55) 0.636 1.84 (0.69 to 4.92) 0.224Smoker versus never-smoker 1.21 (0.56 to 2.65) 0.626 0 (0 to >100) 0.924Tumor status, T2/T3/T4 versus T1 1.67 (0.96 to 2.91) 0.07 >100 (0 to >100) 0.906Nodal status, N1/N2 versus N0 1.47 (0.78 to 2.75) 0.234 2.51 (0.41 to 15.48) 0.321LVI, positive versus negative 2.59 (1.42 to 4.74) 0.002 2.69 (0.82 to 8.86) 0.104Chemotherapy, treated versus untreated 0.81 (0.41 to 1.6) 0.536 0.69 (0.2 to 2.35) 0.551RB pathway, altered versus unaltered 2.26 (1.29 to 3.98) 0.005 5.14 (1.06 to 25) 0.043

(trohoc noitadilaV n ()95 = n = 13)729.0)001> ot 0(001>791.0)71.53 ot 84.0(11.4elam susrev elameF

Age, ≥60 versus <60 0.4 (0.16 to 0.98) 0.044 0 (0 to >100) 0.898Smoker versus never-smoker 2.87 (0.33 to 25.23) 0.341 >100 (0 to >100) 0.954Tumor status, T2/T3/T4 versus T1 0.79 (0.28 to 2.24) 0.659 0 (0 to >100) 0.901Nodal status, N1/N2 versus N0 1.12 (0.33 to 3.74) 0.86 0.38 (0.02 to 8.08) 0.532LVI, positive versus negative 2.42 (0.87 to 6.69) 0.089 >100 (0 to >100) 0.905Chemotherapy, treated versus untreated 2.54 (0.84 to 7.72) 0.099 0 (0 to >100) 0.902RB pathway, altered versus unaltered 5.56 (1.52 to 20.28) 0.009 >100 (0 to >100) 0.945Abbreviations: LVI, lymphovascular invasion; HR, hazard ratio; CI, confidence interval.aCovariates include alterations in the RB pathway and various clinical data such as sex, age, and smoking status.

Early stage (stages I & II) Late stage (stages III & IV)b

bThere is a high possibility that the number of patients in this section is insufficient for an accurate multivariable Cox hazardmodel, and, therefore, the results should be cautiously interpreted (Peduzzi P et al., Journal of clinical epidemiology, 1995,48:1503-10).

Supplementary Table S11.Cox univariable regression analysis of RB pathway alterations.

Parametera HR 95% CI P HR 95% CI P

Discovery cohort (n ()031 = n = 27)447.0)88.2 ot 74.0(61.1735.0)24.1 ot 15.0(58.0elam susrev elameF

Age, ≥60 versus <60 0.92 (0.55 to 1.54) 0.747 1.12 (0.45 to 2.81) 0.804Smoker versus never-smoker 1.26 (0.76 to 2.09) 0.371 0.64 (0.26 to 1.6) 0.34Tumor status, T2/T3/T4 versus T1 1.39 (0.82 to 2.35) 0.222 24.3 (0.02 to >100) 0.393Nodal status, N1/N2 versus N0 2.07 (1.15 to 3.73) 0.015 1.72 (0.39 to 7.49) 0.473LVI, positive versus negative 2.78 (1.62 to 4.78) <0.001 1.8 (0.66 to 4.92) 0.25Chemotherapy, treated versus untreated 1.36 (0.74 to 2.48) 0.32 1.15 (0.41 to 3.23) 0.794RB pathway, altered versus unaltered 2.38 (1.38 to 4.11) 0.002 2.55 (0.77 to 8.44) 0.127

Validation cohort (n ()95 = n = 13)372.0)38.82 ot 93.0(43.3102.0)33.4 ot 37.0(87.1elam susrev elameF

Age, ≥60 versus <60 0.48 (0.2 to 1.16) 0.105 0.09 (0.01 to 0.81) 0.032Smoker versus never-smoker 0.89 (0.37 to 2.16) 0.803 0.48 (0.06 to 4.17) 0.507Tumor status, T2/T3/T4 versus T1 1.29 (0.49 to 3.35) 0.606 0.57 (0.1 to 3.15) 0.519Nodal status, N1/N2 versus N0 1.39 (0.46 to 4.16) 0.558 1.56 (0.28 to 8.58) 0.61LVI, positive versus negative 1.76 (0.68 to 4.61) 0.247 0.94 (0.11 to 8.27) 0.957Chemotherapy, treated versus untreated 1.67 (0.68 to 4.07) 0.264 0.75 (0.09 to 6.49) 0.794RB pathway, altered versus unaltered 3.26 (1.17 to 9.12) 0.024 0.04 (0 to >100) 0.562Abbreviations: LVI, lymphovascular invasion; HR, hazard ratio; CI, confidence interval.aCovariates include alterations in the RB pathway and various clinical data such as sex, age, and smoking status.bThere is a high possibility that the number of patients in this section is insufficient for an accurate univariable Cox hazardmodel, and, therefore, the results should be cautiously interpreted (Peduzzi P et al., Journal of clinical epidemiology, 1995,48:1503-10).

Early stage (stages I & II) Late stage (stages III & IV)b

Detected Sequenom-specific

WES-specific WES sensitivity

EGFR 29 7 4 80.6%KRAS 3 11 6 21.4%PIK3CA 5 3 8 62.5%BRAF 3 0 3 100.0%4 genes combined 40 21 21 65.6%

Supplementary Table S12. Comparison of mutations detected using whole-exome sequencing(WES) and Sequenom in selected genes (EGFR , KRAS , PIK3CA , and BRAF ).

The sensitivity of WES was computed (WES sensitivity) after assuming that the resultsdetermined using Sequenom were true positives. Detected: substitutions detected using bothWES and Sequenom; Sequenom-specific: substitutions detected using only Sequenom; WES-specific: substitutions detected using only WES.

Supplementary Table S13.

Known drivercandidates fromprevious studies

θT: 6.3 (MuTectdefault)

θT: 8.0 (amore strigentparameter)

EGFR 42.9% 42.4%TP53 28.8% 25.3%KRAS 12.4% 11.8%LRP1B 11.8% 9.4%PIK3CA 8.2% 8.2%

RB1 6.5% 5.9%SETD2 5.3% 5.3%

APC 4.1% 3.5%NF1 4.7% 3.5%

PTPRD 3.5% 3.5%BRAF 4.7% 2.9%

EPHA5 3.5% 2.9%GNAS 3.5% 2.9%

SMAD4 3.5% 2.9%PTEN 2.4% 2.4%

CTNNB1 2.4% 1.8%ERBB4 2.4% 1.8%

PDGFRA 1.8% 1.8%RBM10 1.8% 1.8%ARID1A 2.4% 1.2%

ATM 2.4% 1.2%CBL 1.2% 1.2%

EPHA3 2.4% 1.2%INHBA 1.2% 1.2%NTRK3 1.2% 1.2%PAK3 1.8% 1.2%

PPP2R1A 1.8% 1.2%SMARCA4 1.8% 1.2%CDKN2A 1.2% 0.6%FBXW7 0.6% 0.6%GOPC 1.2% 0.6%KDR 1.8% 0.6%LTK 0.6% 0.6%MET 0.6% 0.6%

STK11 1.8% 0.6%BRD3 0.0% 0.0%

FGFR3 0.0% 0.0%FGFR4 0.0% 0.0%KEAP11 0.0% 0.0%

KIAA0427 0.0% 0.0%NRAS 0.0% 0.0%NTRK1 0.6% 0.0%

SLC38A3 0.6% 0.0%USAF1 0.0% 0.0%

ZMYND10 0.0% 0.0%

Prevalence of known driver candidates when usingtwo different MuTect parameters.

Discovery(n = 170)

Validation(n = 77)

I 110 (65%) 47 (61%)II 28 (16%) 16 (21%)III 28 (16%) 12 (16%)IV 4 (2%) 2 (3%)T1 58 (34%) 24 (31%)T2 95 (56%) 41 (53%)T3 9 (5%) 7 (9%)T4 8 (5%) 5 (6%)N0 121 (71%) 58 (75%)N1 27 (16%) 13 (17%)N2 22 (13%) 6 (8%)M0 165 (97%) 75 (97%)M0/1 1 (1%) 1 (1%)M1 4 (2%) 1 (1%)

Metastasis stage

aReflects the tumor, node, and metastasis stage (TNM) according to AJCC 7th edition staging criteria

Supplementary Table S14. Number of patients included in the overall, tumor, node, and metastasis stage categories.

Tumor stage

Overall stagea

Node stage

Ding et al . (2008) Imielinski et al . (2012) Seo et al . (2012)TP53 TP53 EGFRKRAS KRAS KRASSTK11 EGFR NRASEGFR STK11 PIK3CALRP1B KEAP11 BRAF

NF1 ATM CTNNB1ATM NF1 METAPC SMARCA4

EPHA3 ARID1APTPRD BRAF

CDKN2A RBM10ERBB4 SETD2

KDR PIK3CAFGFR4 CBLNTRK1 FBXW7

RB1 PPP2R1ANTRK3 RB1EPHA5 SMAD4

PDGFRA CTNNB1GNAS USAF1LTK KIAA0427

INHBA PTENPAK3 BRD3

ZMYND10 FGFR3NRAS GOPC

SLC38A3

Supplementary Table S15. List of potential cancer driver genes with somaticsubstitutions or small indels determined in previous studies.