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Effect sizes of somatic mutations in cancer Vincent L. Cannataro 1 , Stephen G. Gaffney 1 , Jeffrey P. Townsend 1,2,3† 1 Department of Biostatistics, Yale University, New Haven, CT, 2 Program in Computational Biology and Bioinformatics and 3 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT To whom correspondence should be addressed. 1 . CC-BY-NC 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/229724 doi: bioRxiv preprint

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Page 1: Effect sizes of somatic mutations in cancer · sequenced from that tumor type 15,16 , the statistical significance ( P value ... approaches accounting for genic mutation rates will

Effect sizes of somatic mutations in cancer

Vincent L. Cannataro 1, Stephen G. Gaffney1, Jeffrey P. Townsend 1,2,3†

1Department of Biostatistics, Yale University, New Haven, CT, 2Program in Computational Biology and Bioinformatics and 3Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT †To whom correspondence should be addressed.

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Cancer growth is fueled by genomic alterations that confer selective advantage to somatic cells 1. A major goal of cancer biology is determining the relative importance of these alterations. Genomic tumor sequence surveys have frequently ranked the importance of genetic substitutions to cancer growth by P value or a false-discovery conversion thereof 2,3. However, P values are thresholds for belief 4, not metrics of effect 5,6. Their frequent misuse as metrics of effect has often and ineffectively been vociferously decried 5,7–9, even in cases when the only attributable mistake was omission of effect sizes 10,11. Here, we draw upon an understanding of the development of cancer as an evolutionary process 12,13 to estimate the effect size of somatic variants. We estimate the effect size of all recurrent single nucleotide variants in 23 cancer types, ranking their relative importance within and between driver genes. Many of the variants with the highest effect size per tumor, such as EGFR L858R in lung adenocarcinoma and BRAF V600E in colon adenocarcinoma, are within genes deemed significantly mutated by existing whole-gene metrics. Quantifying the effect sizes of somatic mutations underlying cancer has immediate significance to the prioritization of clinical decision-making by tumor boards, selection and design of clinical trials, pharmacological targeting, and basic research prioritization.

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Since the advent of whole-exome and whole-genome sequencing of tumor tissues, studies of

somatic mutations have revealed the underlying genetic architecture of cancer 14, producing ordered lists

of significantly mutated genes whose ordering implies their relative importance to tumorigenesis and

cancer development. Typically, differentiation of selected mutations from neutral mutations is performed

by quantifying the over-representation of mutations within specific genes in tumor tissue relative to

normal tissue, and disproportionate prevalence of somatic mutations in a gene has been taken as prima

facie evidence of a causative role for that gene. Two quantifications have implicitly ordered the

importance of discovered cancer “driver” genes: the prevalence of the mutation among tumor tissues

sequenced from that tumor type 15,16, the statistical significance (P value) of the disproportionality of

mutation frequency 2, or both 14. Versions of these metrics have shifted from simple ranks by mutation

prevalence in a tumor population 17–19 to calculation of statistical significance of mutation prevalence over

genome-wide context-specific background mutation rates 20–22, to ratios of the prevalence of

nonsynonymous and synonymous mutations 1,23, to P values based on a gene-specific mutation rate and a

diversity of genomic data 3. Although the approaches used to calculate P values have become more

sophisticated, neither prevalence nor P value is an appropriate metric for quantifying the vital role of

genes or their mutations to tumorigenesis and cancer development.

While prevalence of a somatic mutation in a cancer type has important consequences for biomarker

studies 24 and identification of therapeutic population for a targeted therapy 25, there is only a

correlative—rather than causal—link between prevalence of mutation and its contribution to

tumorigenesis and cancer development. The lack of causal linkage is easily seen by considering the

mutated genes that, in spite of their high prevalence in tumor populations, are universally regarded as

false positives. For example, the gene TTN is a structural protein of striated muscle. Because it is long,

because it replicates relatively late in the synthesis phase of mitosis, and because it is inaccessible to

transcription-mediated repair in non-muscle tissues, it has a high mutation rate and is frequently mutated

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in non-sarcoma cancer tissues. While TTN is an extreme example—sometimes showing up at the top of

lists ordered by prevalence of genic mutation 1,3—it exemplifies the problem with using mutation

prevalence as a proxy for importance. Any consideration of whether mutated genes are contributing to

tumorigenesis and cancer development—or of the degree to which they are contributing—must address

the issue of their underlying mutation rate.

The appearance of such biologically implausible genes in ranked mutation lists prompted the

development of increasingly sophisticated statistical approaches designed to “weed out” false-positives

via calculation of a P value that accounted for gene length and background mutation rate. The classical

evolutionary biology approach is to use the frequency of synonymous site mutations in each gene as a

proxy for mutation rate. As in the divergence of species, synonymous site mutations are presumably

neutral (or nearly so) to the success of cancer lineages during the divergence from normal to resectable

tumor. As the number of mutations observed in a given gene is typically much smaller in the somatic

evolution of cancer than is observed in the divergence between most species, use of synonymous sites

within a single gene leads to many genes with zero synonymous mutations in most cancers, and an

ineffective calculation of P value. Alternative approaches currently in use obtain a reasonably robust

estimate of genic mutation rate using correlates such as gene expression levels, chromatin states, and

replication timing, and are largely successful at excluding known false positives 3.

In genomic tumor surveys, the sample size of tumors varies among studies, posing a problem for

comparison of P values within or between cancer types 5,6. An even more serious issue with using P

values for ranking genes or mutations arises from the same source that obviates use of genic mutation

prevalence: the confounding effect of mutation rate. Because the “sample size” of overall mutations in a

gene is dictated by the genic mutation rate, it is much easier for genes with high mutation rates not only to

reach high genic prevalence, but also to reach statistical significance despite small effect sizes. While

approaches accounting for genic mutation rates will eliminate false positives 3, and the P value will serve

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to exclude genes like TTN that have no role in tumorigenesis and cancer development, the rank order by P

value of genes that do have a role in tumorigenesis and cancer development will remain highly affected

by mutation rate. Genes with higher mutation rate will (correctly) be more likely to achieve statistical

significance, and thus will appear deceptively high on a ranked list whose ordering suggests importance in

tumorigenesis and cancer development.

Because genic mutation prevalence and P value inadequately capture importance to tumorigenesis

and cancer development, another metric must be appropriate. To provide an evaluation of the relative

importance of mutations in diverse cancer types to tumorigenesis and cancer development, we called on

an understanding of the development of cancer as an evolutionary process 12,13, and adapted some

straightforward insights from classical evolutionary theory. The cognate metric in evolutionary theory for

quantifying importance to tumorigenesis and cancer development is the selective effect of the mutation on

the cancer lineage. The appropriateness of this metric is fairly easy to recognize. While mutations are the

ultimate source of variation contributing to tumorigenesis, we do not conduct genomic tumor sequence

surveys to discover neutral mutation rates. We conduct them to determine which mutations spread within

cancer tissues because of the effects of mutations on proliferation and survival. Mutation rate is a

confounding phenomenon: when it is high, it also increases prevalence of mutations. Because silent site

substitutions and other correlates of baseline mutation rate provide a means to independently differentiate

silent mutation rate from the impact of natural selection within the tumor, selection intensities can be

estimated, providing the effect sizes of each mutation.

We calculated cancer effect sizes by comparing the rate of observed substitutions to the rate that

substitutions would be expected to arise in the absence of selection 26. In accordance with population

genetic theory, we specify that the rate neutral mutations arise and the rate that they fix as substitutions

within tumors are equivalent 27, and that non-neutral mutations arise at a consistent rate. Thus any

increase in the flux of substitutions among tumors of a particular context above the baseline silent rate

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would be the appropriate estimate of the intensity of selection on that mutation within the tumor

population (Methods).

This calculation yielded cancer effect sizes for all fixed substitutions (Methods, Supplemental

Figure 1, Supplemental Table 1) that quantify contribution to the cancer phenotype within 23 tumor types.

Their relative rank corresponds to their relative importance within the respective tumor types. Several

common known oncogenic substitutions, such as BRAF V600E in COAD and EGFR L858R in LUAD,

and substitutions in known tumor suppressor genes, such as APC in READ and TP53 in HPV-negative

HNSCC, are highly selected, and those genes are also determined as significantly mutated by MutSigCV3.

However, genes determined to be significantly altered in cancer by MutSigCV are well-dispersed within a

large range of site-specific cancer effect sizes (Figure 1), illustrating how discrepant Q values are with

cancer effect size. Several substitutions within genes that are not determined to be significantly

over-mutated via MutSigCV are interspersed among more prevalent substitutions within genes that are

estimated to be significantly mutated, for instance Mastermind-like3 (MAML3) G1069A in READ, a

protein that binds to and stabilizes the DNA-binding complex of the Notch intracellular domain 28 and

Nuclear factor (erythroid derived 2)-like 2 (NFE2L2) R34G in UCEC, a protein that is believed to play a

causative role in squamous cell lung cancer 29,30. Indeed, substitutions in this gene comprise two of the top

three most selected substitutions within our analysis of lung squamous cell carcinoma (Figure 1).

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Figure 1. Cancer effect sizes of recurrent somatic substitutions in eight of the 23 cancer types analyzed. Effect sizes greater than 1 × 103 are indicated by ticks along the tumor-type axes. The highest 50 effect sizes are labeled within each tumor. Names of genes that have more than one mutation within or between tumors are uniquely colored. Genes deemed significantly burdened with mutation 3 are depicted by a red circle next to mutation labels, and the prevalence of each substitution is represented by the size of this circle. LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma. UCEC: Uterine corpus endometrial carcinoma; LGG: Brain Lower Grade Glioma; HNSC: Head and neck squamous cell carcinoma, broken into HPV positive and HPV negative tumor samples (methods); COAD: Colon adenocarcinoma; READ: Rectum adenocarcinoma. NCSNV refers to a non-coding single nucleotide variant outside an exon (e.g. 5’ or 3’ UTRs). HPV+ and HPV– HNSCC have been demonstrated to have significantly different genetic architectures 31, thus they are presented separately.

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Current approaches using conservative P values are particularly underpowered to detect genes that

are of high importance to tumorigenesis and cancer development in some cancer cases because the site or

sites conveying the relevant phenotype are mutated at low rates. For instance, FBXW7 R505G was

estimated to have the highest selection intensity in HPV+ HNSCC, and BRAF V600E was estimated to

have the fifth highest selection intensity in LGG, but both of these genes were classified as not

significantly mutated by MutSigCV within these two cancer types. Mutations within these two

well-known oncogenes were estimated to confer large effect sizes, and these genes were determined to be

significantly mutated in other cancer types, yet their degree of influence within HPV+ HNSCC and LGG

is obscured in cancer-wide significance analyses. The relative effect sizes of cancer mutations can inform

nearly every aspect of basic research related to oncology and should play key roles in clinical

decision-making. They should be integrated into clinical decision-making in tumor boards, where they

would provide crucial insight into the relative upper limits of the efficacy of precision-targeted treatments.

They should guide the selection of clinical trials to target small populations that can benefit from targeted

therapeutics developed for other cancer types. They should guide targeted development of new

therapeutics, indicating the upper limit of effect for a perfect therapeutic ameliorating an oncogenic

mutation. Lastly, they should guide the selection of important areas of basic research that have potential

to lead to therapies and cures for cancer. It does not escape our notice that here we only calculate the

effect size of single nucleotide variants. Importantly, effect sizes of other mutational processes, such as

copy number alterations or epigenetic modifications, could be similarly calculated once estimates of

intrinsic mutation rate in the absence of selection are identifiable for these processes.

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METHODS:

Data acquisition and processing:

All data were obtained either from The Cancer Genome Atlas (TCGA) projects downloaded from

the National Cancer Institute’s Genomic Data Commons 32, or from projects part of the Yale-Gilead

collaboration (Supplemental Table 2). All TCGA data used in this analysis were from GDC version

gdc-1.0.0 and relevant UUID are found in Supplemental Table 3.

All TCGA data were first converted to hg19 coordinates using the liftOver function of the

rtracklayer R package33. To obtain consistency between gene symbols used in MutSigCV and in

mutation data, symbols in the MutSigCV default covariates files were mapped to HUGO symbols.

Non-HUGO symbols were mapped using substitutions from the NCBI Gene database using unambiguous

'synonym' matches; unambiguous 'previous symbol' matches; and manual lookups. CDS coordinates for

each gene were obtained from UCSC's hg19 annotation database. The MutSigCV covariates files, and

gene annotation files used in our analyses can be found at

https://github.com/Townsend-Lab-Yale/SNV_selection_intensity. Nucleotide variants one or two

positions apart in the same tumor sample were removed from the analysis to ensure that we analyzed only

single nucleotide variants. Head and neck squamous cell tumors were designated as HPV positive if they

contained greater than 100 HPV RNA viral transcript reads per hundred million (RPHM) 34 and were

designated positive in clinical data obtained from The Broad GDAC Firehose 35, and tumors were

designated HPV negative if they contained less than 100 HPV RNA viral transcript RPHM and were

designated negative in clinical data obtained from The Broad GDAC Firehose.

Calculating mutation rate and selection intensity:

We define the selection intensity of a single nucleotide variant as the ratio of the flux of fixed

mutations to the expected flux of fixed mutations in the absence of selection. We estimate the expected

rate of fixed mutations in the absence of selection by using MutSigCV to calculate the silent mutation rate

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for each gene. We use the median gene expression from cell lines derived from each analyzed tissue type

(Supplemental Table 3) as the MutSigCV expression covariate for that tissue type. We then normalized

this gene-level rate at every site for every mutation across each gene given the specific trinucleotide

mutation profile of the tissue, such that the average rate of mutations among all trinucleotide

combinations in each gene is equal to the average rate calculated by MutSigCV. The trinucleotide profile

of a tissue is calculated as the average of trinucleotide COSMIC signatures among non-recurrent

mutations calculated by the deconstructSigs R package 36 for all tumors with over 50 single

nucleotide variants.

Formally, we define the rate of non-neutral substitutions, as , where N is the,λ μ u(s)N ×

effective population size of the tumor, is the mutation rate of a substitution, and is the probabilityμ (s)u

the mutation fixes within the population, which is a function of the selection coefficient of the mutation, s .

The probability that a silent mutation fixes is the inverse of the population size within which it fixes, and

thus rate of non-neutral substitutions, divided by the rate of neutral substitutions, is or,λμ = Nμ × 1

N

Nμ × u(s)

, the selection intensity and effect size of the mutation. The term selection intensity is used as a(s)N × u

direct parallel to the classic derivation of “scaled selection coefficient” or “selection intensity” in the

population genetics literature 37–40. Because a tumor sequence is a single snapshot in time, we can only

detect one substitution event per site per tumor. To correct for this issue of detection, we define the rate of

substitution, , as the Poisson rate of occurrence of one or more observed fixation events, i.e. the value ofλ

that maximizes , where is the number of tumors without any substitutionλ e ( −λ) n0 × 1 ( − e −λ) n1 n0

at that site and is the number of tumors with the specified substitution at that site. We definen1

substitutions as single nucleotide variants that are observed in tumors from more than one patient

(recurrent) within our dataset, and we calculate the selection intensity of the recurrently mutated

substitutions to minimize the probability of analyzing passenger mutations.

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Scripts used to perform this analysis are available online at

https://github.com/Townsend-Lab-Yale/SNV_selection_intensity

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Supplementary Information available online. Acknowledgements: This project was supported by Gilead Sciences, Inc. We also thank the Yale High Performance Computing Center for providing computational resources. Author contributions : VLC and JPT designed the analyses. VLC performed the analyses with assistance from SGG. JPT, VLC, and SGG wrote the manuscript. Author Information: The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to [email protected].

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Supplemental figure 1 legend: Selection intensities of recurrent somatic substitutions in 23 cancers. BLCA: Bladder Urothelial Carcinoma; BRCA: Breast invasive carcinoma; Cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD: Colon adenocarcinoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma, broken into HPV+ and HPV- tumor samples using criteria described within the Methods section; KIRC: Kidney renal clear cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SKCM: Skin Curaneous Melanoma, broken into primary skin tumors and metastatic skin tumors; STAD: Stomach adenocarcinoma; THCA: Thyroid carcinoma; UCEC: Uterine corpus endometrial carcinoma.

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C R

302*

AP

C R

213*

NP

HS

1 D

1208

Y

PC

BP

1 L1

00Q

KR

AS

G12

V

AP

C R

216*

C8A

S42

1Y

SLC

5A8

E55

9*

HE

ATR

1 L1

256I

TOP

2A E

1035

*

CC

DC

18 N

CS

NV

AP

1M1

N15

5H

KR

AS

G12

A

AP

C R

283*

AP

C R

348*

AP

C R

564*

AR

ID1A

R19

89*

TP

53 I1

95T

GY

LTL1

B V

553G

PC

YT

1A F

263C

PC

BP

1 L1

02Q

KR

AS

G12

D

NA

A25

S60

6Y

ATP

10B

L76

1H

PD

CD

6IP

R32

7Q

RF

X6

R86

6Q

EYA

2 G

77A

KR

AS

G12

C

TR

PM

7 R

1862

C

AB

CB

1 R

286I

GO

LGB

1 V

1309

L

NR

AS

Q61

R

NR

AS

Q61

K

NR

AS

Q61

L

AP

RT

R87

G

NR

AS

G13

R

BR

AF

V60

0E

CS

DE

1 F

700V

IPM

K L

254V

NR

AS

G12

R

NR

AS

Q61

H

SN

X7

E36

4A

AN

KR

D32

L69

1R

NR

AS

G12

V

SD

CB

P T

268A

EP

RS

I820

L

DH

X32

R51

S

AN

O9

D47

3A

SY

TL2

NC

SN

V

AR

HG

EF

2 R

609G

ZN

F83

0 R

239S

FAM

120A

I302

K

TM

2D3

Y13

6S

KIA

A01

00 R

952L

PPA

T E

11D

SC

UB

E3

L427

V

CD

K4

R24

S

UC

KL1

D47

3H

CP

SF

1 Y

1164

H

TR

IM59

I353

M

FAM

151A

V31

D

US

PL1

A43

5G

PLE

KH

G4B

D33

0A

GP

RA

SP

1 D

145V

SLC

6A2

R21

6G

GC

G N

CS

NV

EIF

4G3

E18

9*

CU

BN

R22

4G

BR

AF

N58

1T

MB

NL3

NC

SN

V

TP

MT

V22

0A

STA

T5A

L35

5P

SLC

25A

46 L

221Q

NR

4A3

NC

SN

V

TLL

2 N

CS

NV

DD

X47

G12

8R

AB

CA

6 N

CS

NV

PIP

4K2A

NC

SN

V

TR

PV

2 L1

27V

CT

RB

2 N

CS

NV

MT

MR

6 F

75C

BR

AF

V60

0E

KIT

K64

2E

NR

AS

Q61

R

NR

AS

Q61

L

RP

S27

NC

SN

V

NR

AS

Q61

K

BR

AF

K60

1E

GN

AQ

Q20

9P

CD

YL2

D31

2E

P2R

Y11

NC

SN

V

BA

GE

2 N

CS

NV

TT

N N

CS

NV

C5

R12

06C

AB

CF

1 R

844*

SN

AP

91 D

723N

SIP

A1L

2 S

1472

L

TP

53 R

342*

CO

L5A

3 N

CS

NV

ZN

F99

NC

SN

V

DD

X27

NC

SN

V

TT

N N

CS

NV

TT

N N

CS

NV

PP

IAL4

A N

CS

NV

SLC

30A

8 L2

90P

TT

N H

1509

3H

KIA

A11

99 G

722E

AQ

P12

B T

16I

CY

P4F

12 P

367S

ZN

F76

8 A

488A

A2M

R14

7C

SA

RS

2 N

CS

NV

AR

NT

S32

1F

MD

N1

A23

8V

CO

L12A

1 R

1414

*

AM

D1

R21

0C

FO

LH1

NC

SN

V

AM

Y1C

K29

3K

RA

C1

P29

S

NS

FL1

C N

CS

NV

MA

GE

A11

S20

8L

DH

TK

D1

P84

2L

SE

RP

INA

1 N

CS

NV

ALP

PL2

G44

8R

GD

F2

F33

6F

RE

G1B

NC

SN

V

RA

SA

L2 P

146L

SY

DE

1 F

703F

NP

HP

1 R

347*

BU

B1

R67

7C

SLC

4A10

R41

0C

AR

ID5B

A10

09G

LHC

GR

NC

SN

V

MA

CC

1 N

CS

NV

RH

OA

Y42

S

VM

O1

NC

SN

V

GA

BR

E N

CS

NV

TT

N N

CS

NV

RN

F17

5 S

215G

PIK

3CA

H10

47R

TT

N N

CS

NV

PIK

3CA

N34

5K

ZC

3H4

E77

9Q

CD

H1

D25

4Y

ZB

TB

7A K

424T

DE

K S

288T

OLI

G2

S88

A

IQS

EC

2 G

283A

TP

53 C

176F

DR

G2

R38

L

KC

NH

2 G

149A

C8B

L13

8R

GN

G7

N45

K

ZD

HH

C8

P45

7A

KR

AS

G12

D

DO

CK

10 K

935M

TP

53 Y

220C

ITG

B1

L378

I

INS

IG2

I63F

CAV

1 N

CS

NV

CT

NN

B1

D32

H

AR

HG

AP

1 N

154I

ZM

YM

5 V

60D

TM

CC

1 N

CS

NV

ER

BB

2 D

769Y

C1o

rf9

NC

SN

V

PIK

3CA

E54

5K

ER

BB

2 N

CS

NV

C8o

rf42

W89

G

DE

FB

104A

E25

A

TP

53 P

151T

MLH

1 R

659*

ER

BB

2 S

310F

RP

L5 M

208T

ZH

X3

N24

9K

WT

1 N

CS

NV

NA

LCN

NC

SN

V

DK

K2

NC

SN

V

GA

RT

Q24

1R

STA

G1

A11

6T

TP

53 V

272M

BR

AF

V60

0E

NR

AS

Q61

R

ST

XB

P3

NC

SN

V

MR

PS

16 N

CS

NV

HR

AS

Q61

R

NR

AS

Q61

K

INT

S2

Y57

8N

SPA

CA

3 N

CS

NV

EIF

1AX

NC

SN

V

MA

RC

H4

NC

SN

V

GN

B2L

1 N

CS

NV

BR

AF

K60

1E

EIF

1AX

G9R

KR

AS

G12

V

KIA

A11

09 V

825F

PLC

L1 V

442I

ATP

6V0A

1 N

CS

NV

PM

PC

B N

CS

NV

EIF

1AX

G9D

HR

AS

Q61

K

MC

PH

1 K

645N

DN

AH

10 I1

660M

TB

C1D

12 N

CS

NV

OG

T P

120L

CC

2D2B

NC

SN

V

PP

P2R

1A A

273V

NU

P93

Q15

*

AK

T1

E17

K

DG

CR

8 E

518K

TB

C1D

12 N

CS

NV

TP

53 Q

192*

OR

52E

2 A

251A

PT

EN

R13

0G

SP

I1 N

CS

NV

NF

E2L

2 R

34G

FG

FR

2 S

252W

VN

1R5

K35

7M

AR

ID1A

R22

36P

PP

P2R

1A P

179R

XPA

NC

SN

V

C2C

D4A

NC

SN

V

CN

O N

CS

NV

MB

D2

NC

SN

V

ST

X5

NC

SN

V

AB

HD

14A

F19

1C

GC

SH

K16

9Q

SS

X2I

P N

CS

NV

RA

B6A

NC

SN

V

ME

TT

L14

R29

8P

P2R

Y13

NC

SN

V

TS

PAN

10 P

115A

SO

X17

S40

3I

ZB

TB

6 T

357I

CB

R3

NC

SN

V

RS

RC

1 S

239Y

FB

XW

7 R

505G

CN

O N

CS

NV

FO

LH1B

NC

SN

V

SO

S1

N23

3Y

DD

X1

NC

SN

V

CN

O N

CS

NV

B2M

NC

SN

V

MO

RC

3 R

896P

C16

orf9

1 N

CS

NV

PC

MT

D1

R16

2Q

PO

LE P

286R

AR

L6IP

6 N

CS

NV

PIK

3CA

H10

47L

LTV

1 R

377*

MU

C2

E26

12K

C2C

D4A

NC

SN

V

INS

M1

NC

SN

V

INS

M1

NC

SN

V

JUN

D N

CS

NV

TM

EM

37 E

165K

DLE

U7

NC

SN

V

ZN

F75

A R

134I

ZN

F75

A R

181I

SO

CS

2 R

96Q

AD

O N

CS

NV

GB

X2

NC

SN

V

AU

P1

NC

SN

V

FB

XW

7

HR

AS

TB

C1D

12

TP

53

FG

FR

3

SO

RB

S1

EX

OC

4

NF

E2L

2

TB

C1D

12

HR

AS

FG

FR

3

PIK

3CA

PIK

3CA

TP

53

KR

AS

TP

53

DS

T

FG

FR

3

MD

GA

2

HR

AS

FG

FR

3

FG

FR

3

TT

N

KR

AS

PIK

3CA

C10

orf7

6

EX

OC

4

TP

53

TP

53

LDH

AL6

B

TP

53

TP

53

PIK

3CA

TP

53

CN

TN

AP

3

MLL

3

TP

53

FR

MD

7

C1o

rf12

9

ER

BB

2

CD

H1

TP

53

TP

53

TP

53

PIK

3CA

ALD

OA

SU

PT

6H

PIK

3CA

TP

53

TP

53

AK

T1

FB

XW

7

ALD

OA

ME

D12

GA

BPA

FB

XW

7

CA

CN

G7

ST

6GA

L1

AK

T1

PIK

3CA

EP

300

PIK

3CA

NF

E2L

2

KR

AS

PIK

3CA

KR

AS

NX

F1

IQS

EC

2

MA

PK

1

BR

AF

TP

53

VN

1R5

KR

AS

KR

AS

KR

AS

AP

C

PIK

3CA

PC

BP

1

AP

C

PIK

3CA

KR

AS

KR

AS

AP

C

PIK

3CA

KR

AS

AB

CA

6

AP

C

NR

AS

AP

C

AP

C

AP

C

PIK

3CA

CN

TN

AP

3

EYA

2

TP

53

TP

53

PIK

3CA

SU

PT

6H

ST

6GA

L1

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TT

N

PIK

3CA

AB

CB

1

TP

53

DN

AH

10

TP

53

TP

53

C10

orf7

6

ER

BB

2

TP

53

TP

53

TP

53

AS

PH

AS

PH

BR

AF

TP

53

NR

AS

TP

53

EG

FR

PT

EN

EG

FR

TP

53

PIK

3R1

TP

53

PIK

3R1

EG

FR

PT

EN

EG

FR

TP

53

TP

53

PIK

3R1

MU

C2

FAM

120A

TP

53

EG

FR

TP

53

PIK

3CA

FB

XW

7

FG

FR

3

PIK

3CA

EP

300

PIK

3CA

PIK

3CA

MA

PK

1

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

FB

XW

7

HR

AS

TP

53

TP

53

PIK

3CA

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

HR

AS

TP

53

TP

53

TP

53

HR

AS

TP

53

TP

53

CD

KN

2A

MA

P2

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

RA

C1

ITG

B1

CD

KN

2A

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

TP

53

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

VH

L

PB

RM

1

PB

RM

1

KIT

KR

AS

FLT

3

GA

RT

IDH

1

IDH

2

CA

CN

G7

FLT

3

NR

AS

IDH

1

DN

MT

3A

DN

MT

3A

NR

AS

RU

NX

1

IDH

2

RU

NX

1

IDH

1

IDH

1 IDH

1

EG

FR

BR

AF

TP

53

IDH

1

TP

53

TP

53

TP

53

EG

FR

TP

53

TP

53

TP

53

IDH

2

PIK

3CA

IDH

2

EG

FR

PIK

3CA

PIK

3CA

TP

53

PIK

3CA

TP

53

TP

53

PIK

3CA

EG

FR

TP

53

TP

53

TP

53

TP

53

IDH

2

PIK

3R1

C10

orf7

6

IDH

2

TP

53

TP

53

TP

53

TP

53

NR

AS

PIK

3CA

TP

53

CT

NN

B1

CT

NN

B1

NX

F1

CT

NN

B1

CT

NN

B1

CT

NN

B1

LRR

C16

B

CT

NN

B1

CT

NN

B1

TP

53

CT

NN

B1

TP

53

TP

53

TP

53

CT

NN

B1

EG

FR

CT

NN

B1

CT

NN

B1

CT

NN

B1

BR

AF

KR

AS

CT

NN

B1

HE

ATR

1

KR

AS

BR

AF

KR

AS

KR

AS

EG

FR

EG

FR

CP

SF

1

BR

AF

PIK

3CA

PIK

3CA

FLR

T3

SO

S1

MU

C2

TP

53

TP

53

FLR

T3

PIK

3CA

TP

53

NF

E2L

2

NF

E2L

2

TP

53

TP

53

TP

53

TP

53

TP

53

CD

KN

2A

TT

N

TP

53

TP

53

MD

GA

2

TP

53

TP

53

TP

53

DS

T

CD

KN

2A

TP

53

IQS

EC

2

FB

XW

7

TP

53

TP

53

CD

KN

2A

NF

E2L

2

TP

53

TP

53

TP

53

TP

53

EP

300

TP

53

TP

53

TP

53

TP

53

TP

53

GA

BPA

FR

MD

7

MA

P2

TOP

2A

MA

ML3

TP

53

TP

53

TP

53

LDH

AL6

B

TP

53

TP

53

TP

53

LRR

C16

B

SO

RB

S1

NR

AS

TP

53

TP

53

TP

53

TP

53

C1o

rf12

9

TP

53

TP

53

KR

AS

KR

AS

KR

AS

KR

AS

TP

53

KR

AS

TP

53

TP

53

TP

53

TP

53

TP

53

CD

KN

2A

TP

53

TP

53

TP

53

SM

AD

4

SM

AD

4

TP

53

CD

KN

2A

SM

AD

4

TP

53

GN

AS

TP

53

TP

53

SM

AD

4

TP

53

GN

AS

TP

53

TP

53

CD

KN

2A

TP

53

TT

N

ER

BB

2

SP

OP

SP

OP

SP

OP

SP

OP

SP

OP

SP

OP

SP

OP

TP

53

SP

OP

TP

53

SP

OP

RH

OA

SP

OP

SP

OP

SP

OP

TP

53

BR

AF

SP

OP

ME

D12

HR

AS

CT

NN

B1

TP

53

ME

D12

MLL

3

PIK

3CA

TP

53

PIK

3CA

IDH

1

AK

T1

TP

53

TP

53

DN

AH

10

TP

53

AP

C

AP

C

AP

C

AP

C

MA

ML3

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

AP

C

PC

BP

1

KR

AS

AP

C

HE

ATR

1

TOP

2A

KR

AS

AP

C

AP

C

AP

C

AR

ID1A

TP

53

PC

BP

1

KR

AS

EYA

2

KR

AS

AB

CB

1

NR

AS

NR

AS

NR

AS

NR

AS

BR

AF

NR

AS

NR

AS

NR

AS

FAM

120A

CP

SF

1

BR

AF

AB

CA

6

BR

AF

KIT

NR

AS

NR

AS

NR

AS

BR

AF

TT

N

TP

53

TT

N

TT

N

TT

N

RA

C1

RH

OA

TT

N

PIK

3CA

TT

N

PIK

3CA

CD

H1

IQS

EC

2

TP

53

KR

AS

TP

53

ITG

B1

CT

NN

B1

ER

BB

2

PIK

3CA

ER

BB

2

TP

53

ER

BB

2

GA

RT

TP

53

BR

AF

NR

AS

HR

AS

NR

AS

EIF

1AX

BR

AF

EIF

1AX

KR

AS

EIF

1AX

HR

AS

DN

AH

10

TB

C1D

12

PP

P2R

1A

AK

T1

TB

C1D

12

TP

53

PT

EN

NF

E2L

2

VN

1R5

AR

ID1A

PP

P2R

1A

C2C

D4A

CN

O

FB

XW

7

CN

O

SO

S1

CN

O

PIK

3CA

MU

C2

C2C

D4A

INS

M1

INS

M1

ZN

F75

A

ZN

F75

A

nTCGA = 137

nTCGA = 143

nTCGA = 184

nTCGA = 178 nYG = 50

nTCGA = 336

nTCGA = 289 nYG = 56

nTCGA = 393

nTCGA = 399

nTCGA = 364 nYG = 41

nTCGA = 412

nTCGA = 65

nTCGA = 412

nTCGA = 436

nTCGA = 437

nTCGA = 495

nTCGA = 508

nTCGA = 492 nYG = 22

nTCGA = 363 nYG = 156

nTCGA = 104 nYG = 47

nTCGA = 530 nYG = 23

nTCGA = 492 nYG = 108

nTCGA = 567 nYG = 108

nTCGA = 986

READ

LAML

ESCA

PAAD

KIRC

CESC

GBM

COAD

LIHC

BLCA

HPV+ HNSC

HPV− HNSC

OV

STAD

PRAD

LGG

THCA

SKCMM

SKCMP

UCEC

LUSC

LUAD

BRCA

103104105106107

Selection intensity

Tum

or ty

pe

Q value

Q ≤ 0.1

Q > 0.1

Prevalence

0.60

0.40

0.20

0.10

0.05

0.01

Supplemental Figure 1

.CC-BY-NC 4.0 International licenseis made available under aThe copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It. https://doi.org/10.1101/229724doi: bioRxiv preprint