Coding and noncoding mutations across cancer...

Coding and noncoding mutations across cancer types

Ekta Khurana, PhD Assistant Professor

Meyer Cancer Center, Institute for Computational Biomedicine & Institute for Precision Medicine

Department of Physiology and Biophysics Weill Cornell Medicine, New York, NY

1

[email protected]

@ekta_khurana

2

First cancer WGS,

Ley, Mardis, et al., Nature, 2008

~500 cancer WGS Alexandrov, et al.,

Nature, 2013

~2,800 WGS from ICGC/TCGA

2014

Number of cancer whole genomes sequenced

Genomic variants identified from sequencing

3

ATGAACTGCAATTTCCAGAAGCATGCACCCTTGGAAG - - - TCTA

ATGAACTGCAAATTCCAGAAGCATG - - - - CTTGGAAGAGTTCTASNP

Human Ref.

Dele=on Inser=on Small Indels < 50 bp

Inser=ons

Dele=ons

Duplica=ons Inversion

Large structural variants

Human Ref.

An average human genome contains ~4 million inherited variants and a tumor genome contains thousands of somaMc

variants.

Khurana et al, Nature Rev Genet, 2016

IdenMfying mutaMons associated with cancer

Signals of positive selection Mutation frequency of cancer genes

6

Source: Interna=onal Cancer Genome Consor=um (dcc.icgc.org)

Computational methods to predict functional impact of missense mutations

Gnad et al, BMC Genomics, 2013

8

Most variants are

in noncoding

regions


MB: medulloblastoma DLBC: B cell lymphoma STAD: gastric BRCA: breast PAAD: pancrea=c PRAD: prostate LIHC: liver PA: pilocy=c Astrocytoma LUAD: Lung adenocarcinoma

9

CGGAGG

CGGAAG mRNA

Gain-of-motif TF

TF 0.0

1.0

2.0WT

Mutated

Loss-of-motif

Loss-of-motif

TATCTAT X

TF

TATTTAT TF

WT

Mutated

T0.0

1.0

2.0

CGT

ATG

TGCA5G

CTA

CTG

ATT10AG

TGA

G

ACT

CGAT

ACT15GTA

C

Altered binding effects 2.0

Promoter Gene

X

•  MYB mo=f created & drives TAL1

overexpression in T-‐ALL (Mansour et al, Science, 2014)

Noncoding mutations can be significant drivers Transcription factor (TF) binding disruption

TERT promoter mutated in many different cancer types

Killela et al, PNAS, 2013 Horn et al, Science, 2013 Huang et al, Science, 2013

Noncoding elements in the genome

10


11


Noncoding variants act via tissue-specific regulatory networks

Need to account for heterogeneity of mutation rate in

cancer cells when identifying drivers

•  Histone modification marks

•  DNase I hypersensitive sites

•  Replication timing

Polak et al. Nature 518, 360-364 (2015)

12

Co-variates of mutation rates: Increased mutation density at TF binding sites in melanoma and lung

cancer

13

Perera et al, Nature, 2016 Sabarinathan et al, Nature, 2016 Khurana, Nature News & Views, 2016

Cuykendall et al, COISB, 2017


IdenMfying mutaMons associated with cancer

FunSeq

15

EvoluMonary conservaMon -‐  Typically defined by comparison across species

ConservaMon among humans -‐ Deple=on of common variants/Enrichment of rare variants

1

2

4

3

Estimating negative selection

Common variant Rare variants

Frac=on of rare variants = (Num of rare variants/ Total num of variants)

Enrichment of rare SNPs as a metric for negative selection

16

0.55

0.60

0.65

0.70

0.4

0.5

0.6

0.7

0.8

0.9

A

Frac

tion

of ra

re S

NPs

(non

syn)

All C

odin

g

LOF-

tol.

Rece

ssive

GW

AS

Dom

inan

t

Esse

ntia

l

Canc

er

B

Frac

tion

of ra

re S

NPs

DHS Coding

Bone

Urot

heliu

mEm

br s

tem

cel

lBr

east

Bloo

dPa

ncr d

uct

Panc

reas

Skin

Eye

Live

rM

uscle

Bloo

d ve

ssel

Hear

tLu

ngBl

astu

laM

yom

etriu

mEp

ithel

ium

Pros

tate

Brai

nEm

br lu

ngG

ingi

val

Kidn

eySp

inal

cor

dFo

resk

inCo

nnec

tive

Adip

ose

Live

rCe

rvix

Trac

hea

Sple

enPr

osta

teEs

opha

gus

Lung

Test

isPl

acen

taBl

adde

rCo

lon

Hear

tTh

yroi

dTh

ymus

Kidn

eyBr

ain

Ova

ry

(rare=derived allele freq < 0.5%)

•  Depletion of common polymorphisms in regions under selection

Negative selection restricts the allele frequency of deleterious mutations.

•  Results for coding genes consistent with known phenotypic impacts

•  Other metrics for selection •  Evolu=onary conserva=on

(e.g. GERP) •  SNP density

(confounded by muta=on rate)

LOF-‐tol (Loss-‐of-‐funcMon tolerant): least negaMve selecMon Cancer: most selecMon Khurana et al., Science, 2013

Organism-level negative selection in noncoding elements

17 Khurana et al., Science, 2013

Which noncoding categories are under very strong “coding-like” selection ?

q  Top categories among ranked 102 categories

q  Binding peaks of some general TFs (eg FAM48A)

q  Core mo=fs of some TF families (eg JUN, GATA)

q  DHS sites in spinal cord and connec=ve =ssue

~0.4% genomic coverage (~ top 25)

~0.02% genomic coverage (top 5)

~400-‐fold

~40-‐fold

Enrichment of know disease-‐causing muta=ons from Human Gene Muta=on

database

Khurana et al., Science, 2013 18

19

Identification of noncoding mutations with high impact: FunSeq

FunSeq2: Feature weight - Weighted with mutation patterns in natural polymorphisms

(features frequently observed weighed less) - entropy based method

!! = 1+ !!!"#!!! + 1− !! !"#! 1− !! !!!

!!"#$%! = ! !!!!!!!"!!"#$%&$'!!"#$%&"'!

20

HOT region

Sensi=ve region

Polymorphisms

Genome

p = probability of the feature overlapping natural polymorphisms

Feature weight:

For a variant:

wdp

Fu et al., Genome Biology, 2014 hgps://github.com/khuranalab/FunSeq_PCAWG

IdenMfying noncoding variants

associated with cancer

CNCDriver FunSeq

22

CNCDriver for detecting driver coding & noncoding elements

CNCDriver results in lung cancer (n=84)

23

Functional validation of candidates in prostate cancer

WDR74 promoter

q  Sanger sequencing in 19 additional samples confirms the recurrence

q  WDR74 shows increased expression in tumor samples

benign

PCa

24

RET promoterIncreased activity

EIF4EBP3 promoterReduced activity

25

International Cancer Genome Consortium & The Cancer Genome Atlas

~2800 WGS (tumor & normal), ~1500 RNA-‐Seq, ~1400 methylaMon

26

Acknowledgements

Yale Yao Fu (now at Bina), Xinmeng Mu (now at

Broad), Jieming Chen, Lucas Lochovsky, Arif Harmanci, Alexej Abyzov,

Suganthi Balasubramanian, Cristina Sisu, Declan Clarke, Mike Wilson, Yong Kong, Mark

Gerstein

Sanger Vincenza Colonna, Yuan Chen, Yali Xue, Chris

Tyler-Smith

Cornell Steven Lipkin, Jishnu Das, Robert Fragoza,

Xiaomu Wei, Haiyuan Yu

Andrea Sboner, Dimple Chakravarty, Naoki Kitabayashi, Vaja Liluashvili,

Zeynep H. Gümüş, Kellie Cotter, Mark A. Rubin

U of Michigan Hyun Min Kang U of Geneva

Tuuli Lappalainen (NYGC), Emmanouil T. Dermitzakis

Baylor Daniel Challis, Uday Evani, Donna

Muzny, Fuli Yu, Richard Gibbs EBI

Kathryn Beal, Laura Clarke, Fiona Cunningham, Paul Flicek, Javier Herrero, Graham R. S. Ritchie

Boston College Erik Garrison, Gabor Marth

Mass Gen Hospital Kasper Lage, Daniel G. MacArthur,

Tune H. Pers Rutgers

Jeffrey A. Rosenfeld

FuncMonal InterpretaMon

Group ~50 par=cipants

~40 Ins=tutes ~550 par=cipants

Khurana lab Eric Minwei Liu Priyanka Dhingra Alexander Fundichely Tawny Cuykendall Andre Forbes Mark Rubin Dimple Chakravarty Kellie Cotter David Rickman Adeline Berger Andrea Sboner Deli Liu Steve Lipkin PCAWG (ICGC/TCGA) collaboration (~100)

Acknowledgements

Coding and noncoding mutations across cancer...

Documents

Transcript of Coding and noncoding mutations across cancer...