Integrative analysis of lung cancer etiology and risk - PowerPoint Presentation

Slide1

Integrative analysis of lung cancer etiology and risk

Project 1 – Chris Amos and James McKayProject 2 – Paul Brennan and Mattias JohanssonProject 3 – Rayjean HungAdministrative Core – Chris Amos and Rayjean HungBiostatistics Core – Xihong LinSteering Committee: Loic Le Marchand, Neil Caporaso, M. Tere Landi, David Christian, John Field,

April 16, 2018

Slide2

Background

Lung Cancer is the leading cause of Cancer Death with 224,210 cases in 2014 and 4x number of deaths from breast cancer

Lung cancer results from exposure to tobacco smoke and other environmental carcinogens in a genetic milieu that strongly influences smoking behaviors and risk for cancer development

Early stage lung cancers have a much better prognosis (25-30% 5-year mortality compared to >90% for stage III-IV)

Slide3

Background

The NLST showed a 20% reduction in lung cancer – specific mortality and 7% reduction in overall mortality

Many positive scans in the CT screened arm with 50 positive findings per true cancer identified.

The NLST also showed a dramatic stage shift with 77% stage I-II cancers in CT screened arm versus 39% stage I-II cancers in the X-ray arm

Cost to Medicare for implementing USPTF guidelines estimated to be between $1.3 billion and $2 billion/year

Slide4

Rationale

Our work shows that carcinogenic load and success in tobacco cessation relates to genetic factorsLimited number of studies targeting analysis and identification of early stage lung cancersNeed to better characterizing molecular mechanisms increasing lung cancer riskNeed for biomarkers and models to define highest risk individuals to pursue screeningDeveloping biomarkers to assist in resolving positive findings will reduce patient burden and cost of screeningIntegrates activities from LC3, EDRN, GAME-ON towards understanding development and detection of early stage lung cancer

Slide5

Preliminary Data

Developed novel risk models for predicting lung cancer risk with higher accuracy than models used by the NLST (Tamemagi et al., N Engl J Med. 368:728-36, 2013)

Identified surfactant protein B as playing a major role in lung cancer development (

Takugi

et al, CEBP 22:1756-61, 2013), and low B6 and folate increasing risk (J Natl Cancer Inst. 2018 Jan 1;110(1).

doi

: 10.1093).

Developed novel approaches for pathway analysis (Freytag et al., Hum

Hered

. 76:64-7, 2013), gene-environment interaction analysis(Liu et al. Hum Genet. 2014, Li et al. Carcinogenesis. 2018 Mar 8;39(3):336-346) mediation analysis for CHRNA5 (

VanderWeele

et al, Am J Epidemiol. 175:1013-20, 2012) and Mendelian Randomization for BMI (

PLoS

One. 2017 Jun 8;12(6):e017787), telomere length (JAMA Oncol. 2017 May 1;3(5):636-651) and polyunsaturated fatty acids (Carcinogenesis. 2017 Oct 26;38(11):1147-1154).

Performed largest GWAS of lung cancer (Nat Genet. 2017 Jul;49(7):1126-1132.). Performed a systematic review of existing GWAS studies

Slide6

Overview of Hypotheses

Like other common cancers lung cancer develops through a multistep process with early stages relating to exposures and initiating eventsDeveloping a comprehensive understanding of the intersection of genetic and environmental factors along with early changes in lung cancer will allow us to build effective risk models to identify people at highest risk for lung cancer development and to assist in resolving positive screens.

Slide7

Program Objectives

To support an international consortium studying early stages of lung cancer developmentTo deploy research that characterizes the relevance of existing biomarkers for lung cancerTo develop and evaluate existing and new models for risk assessment using epidemiological, genetic and biomarker informationTo provide data to broadly from large scale genetic and epidemiological investigations to the research community

Slide8

Overview of the research project

Slide9

Status of Grant Funding

P01 Grant submitted in May, 2016 after initial submission, May 2015Positive review received in December 2016Required extensive commentary finally approved for funding April, 2017Initial awarding of the NOA delayed until August, 2017 due to transfer to U19 mechanismI decided to move to Baylor College of Medicine at the end of AugustDecision to Transfer U19 to BCM on move, November 1, 2017850 page transfer application completed in December, 2017.Completed transfer application accepted around Feb 1, 2018Final negotiations to receive new NOA 4/17/2018 with 4/1/2018 start date

Slide10

Aims/Deliverables Administrative core

Aim 1: Maintain and further develop a database for epidemiological, genetic and biomarker data.Aim 2: Provide Integrative support for U19 ActivitiesAim 3: Ensure Compliance with regulatory requriements.Aim 4: Provide Fiscal Oversight. Deliverables, this year: Data Harmonization is ongoing at U Toronto. Routine management of genomic data. Data will have to transfer to Baylor at some point in late summer. Extending computational environment at Baylor. Hired new administrator at Baylor to facilitate support. Hiring students to build out the INTEGRAL website. Fiscal oversight has been impossible during the transfer but hopefully finished (with a new April 1 start date). Completing the NLST reapplication and begin processing NLST samples at U. Toronto.

Slide11

Cores

Administrative Core – Chris Amos and Rayjean HungSupports all interactions among investigatorsSupports project developmentSupports a website for tracking projectsSupports travelSupports uploading of data to dbGAP or GEOMultiple PI mechanism will provide internal guideanceExternal Advisory panel provides guidance

Slide12

OncoArray Proposal Summary

Scale

Focus

Risk

Prognosis

Both

Method

Other

phenotype

Inter-actions

Total

GWAS

21

5

2

2

3

2

35

Targeted

14

1

3

1

0

0

19

Gene-Set

14

2

2

1

1

0

20

Specific

pathway

16

2

1

0

0

0

19

Total

65

10

8

4

4

2

93

Slide13

https://oncoarray

.dartmouth.edu/pending.php1/1

4/16/2018 Oncoarray

Proposals

in

Pending

Onco

Array

P

r

oposals -

Pending App

r

oval

Note: Earliest ones a

r

e listed at the top. Please

r

efe

r

ence pCode for futu

r

e discussion.

Last

updated

on: April

1

1, 2018

Registration Date

pCode

T

itle

Nov

1

1,

2014

2014

1111

1543-

Christiani

Functional

and

rare

g

enetic

variants

for

lung

cancer

p

rognosis

Jan 14, 2016

20160

1

141302-

Capasso

Shared

g

enetic

susceptibilit

y

among

neural

crest

cell-derived

cancers

Mar 07,

2016

20160307

1

136-

Y

u

Explore

disease-pathwa

y

association

using

SNP-level

summar

y

statistics

Dec 15,

2016

201612151835-

Sun

Stud

y

of

g

enetics

of

associations

between

alle

r

g

ies

and

cancers

Jan 26, 2017

201701261635-

W

ei

Association

between

SNPs

in

g

enes

involved

in

nutrient

metabolism

p

athways

and

lung

cancer

risk

–

A

meta-analysis

Feb 16, 2017

201702162252-

Xiao

Candidate

Gene-Environment

Interaction

Analysis

for

Common

and

Rare

V

ariants

Oct 25, 2017

201710250243-

Christiani

Integrative

OncoArra

y

analysis

for

la

r

g

e-scale

transcriptome-wide

association

studies

for

non-small

cell

lung

cancer

survival

Dec 06,

2017

201712061717-

Dimou

Mendelian

randomization

stud

y

of

sex

steroid

hormones

and

reproductive

factors

and

risk

of

lung

cancer

Feb 01, 2018

2018020

11

123-

Park

Genetic

determinants

of

smoking

behavior

and

risk

of

lung

cancer

Apr 09, 2018

201804091407-

Christiani

Causal

mediation

analysis

of

g

enetic

variants,

smoking

,

and

lung

cancer

risk

Apr 09, 2018

201804091517-

Dragani

Identification

of

g

enetic

variants

associated

with

age

at

diagnosis

of

lung

adenocarcinoma

p

atients

Apr 10, 2018

201804101054-

Christiani

Functional

analysis

of

g

enetic

variants

related

to

lung

cancer

Apr 10, 2018

20180410

1

101-

Christiani

Genetic

contributions

to

lung

cancer

risk

conveyed

through

inflammator

y

and

immune

p

athways

Apr 10, 2018

201804101434-

Chen

Genetic

Risk

Scores

of

Smoking

Behaviors

Predict

Delayed

Smoking

Cessation

and

Earlier

Lung

Cancer

Slide14

Aims/Deliverables Biostatistics core

Aim 1: Ensure rigor of biostatistical and bioinformatics approaches.Aim 2: Provide expertise in design and analysis in statistical genetics, genomics, bioinformatics and machine learning for all Projects. Aim 3: Conduct mission related statistical methods (pathway analysis, mediation, and support mendelian randomization, also give guidance in genomics).Aim 4: Disseminate statistical methodology via articles and web based software.Aim 5: Provide education to students and researchers. Deliverables this year: Assistance with mediation analyses will be valuable. Further development of models for mediation involvning multiple endpoints?

Xihong

and Sun also worked on a pathway based analysis leading to a paper on inflammation. Working on risk modeling is important ongoing requirement.

Slide15

Aims/Deliverables Project 1

Aim 1: Characterize contributions of common genetic variation to lung cancer etiology.Aim 2: Investigate role of rare variants in lung cancer susceptibility. Aim 3: Identify genetic effects on smoking behaviorAim 4: Characterize joint effects of environmental and genetic interactions on lung cancer riskDeliverables Year 1: 1. Need to complete analyses by ethnic subpopulation and consider comparative and random effects models. 2. Selected Initial analyses of rare variants are completed along with some methodology approaches by Hung and McKay groups. 3. Smoking behavior GWAS impact being studied for lung cancer risk by Chen and Bierut, effects of smoking on carcinogenesis being pursued by LeMarchand. 4. Preliminary Interaction analyses completed by Li et al, but extremely conservative approach was used and should be repeated. 5. Supplement to study BRCA2 may be funded (hopeful, but could resubmit). 6. Need to complete NLST revision. 7. Need to integrate additional GWAS from GELCC and Asian studies.

Slide16

Aims/Deliverables Project 2

Aim 1: To organize the LC3, to study 2,300 former and current smoking LC cases that were diagnosed within 5 years of donating their blood sample along with one smoking-matched control per case;Aim 2: To replicate a comprehensive panel of promising risk biomarkers and identify those that may be useful for risk prediction. Aim 3: To extensively evaluate all replicated risk biomarkers from Aim 2, identifying a minimum set of validated risk biomarkers, and ultimately evaluate the extent to which they improve risk prediction models.

The final outcome of this work will be risk prediction models incorporating a distinct set of biomarkers that predict LC risk, and these biomarkers will finally be evaluated in CT screening studies in collaboration with Project 3.

Deliverables this year: Organize samples for LC3, present a standardized approach for evaluating biomarkers, mendelian randomization of selected potential biomarkers?

Slide17

Aims/Deliverables Project 3

Specific Aim 1 will establish an integrated risk prediction model to identify individuals at high risk of lung cancer, initially analyzing epidemiological and smoking related phenotypes in the first years but then integrating targeted biomarker, genomic profile, and lung function data applied to LC CT screening populations with a total of 950 CT-detected LC patients with biosamples from 46,057 screening individuals (including 9,759 in Canada, 26,722 in NLST, and 9,576 in Europe. The clinical utility of the models will be assessed by net benefit and decision curve analysis. As a result of these analyses we will (4) develop a risk calculator for use in clinical settings. Specific Aim 2 will establish a comprehensive LC probability models for individuals with LDCT-detected non-calcified pulmonary nodules. In this aim we will (a) first establish the 2Ddiameter-based probability model in N. American CT programs based on 36,481 participants, and then externally validate it based on 9,576 participants in the European LDCT programs; (b) establish the volume3D and radiomics-based probability model in European CT programs based on 9,576 participants in European CT programs, and then externally validate it in the North American CT screening populations; and (c) assess the added predictive value and clinical usefulness of targeted genomic and molecular profiles in both the 2D diameter- and 3D and radiomics volume-based LC probability models based on risk stratification table analysis and decision curve analysis. Finally we will (d) compare the model performance with the existing classification system such as Lung-RADS.

Deliverables: 1. Organizing the screening populations, 2. harmonizing images

Slide18

Available RFAs - PAR-18-752

Administrative Supplements to NCI Grant and Cooperative Agreement Awards to Support Collaborations with the Drug Resistance and Sensitivity Network (DRSN)This FOA supports supplemental funds to current NCI-funded research projects for new interdisciplinary collaborations between non-U54 investigators and DRSN U54-supported investigators to perform research within the scientific scope(s) of the parent grant and/or cooperative agreement award(s) that will lead to improved pre-clinical evaluations of novel discoveries in cancer drug resistance that could ultimately be tested in NCI-sponsored clinical trials.  Identifying potential biomarkers that could be used to monitor or select patients for therapy, based on sensitivity or resistance to the planned therapy.Up to 250K for 1 year (total cost)Due date: June 30, 2018, by 5:00 PM local time of applicant organization.

Slide19

Available RFAs - RFA-CA-18-010

Revision Applications to National Cancer Institute's (NCI) Supported P01 Awards to Include Research on the NCI's Provocative QuestionsPQ1: What molecular mechanisms influence disease penetrance in individuals who inherit a cancer susceptibility gene?PQ3: Do genetic interactions between germline variations and somatic mutations contribute to differences in tumor evolution or response to therapy?PQ6: How do circadian processes affect tumor development, progression, and response to therapy?The budget may not exceed $150,000 in direct cost per year.Our prior proposal received a score of 30, so it depends on what the other scores in the pool were (total of 750K assigned in FY2019).

Due dates June 29, October 30, 2018.

Slide20

Available RFAs - RFA-CA-18-010

The purpose of this Funding Opportunity Announcement (FOA) is to encourage revision applications from currently funded NCI P01 program projects proposing to expand upon the original research question(s) or otherwise accelerate progress for the parent study by incorporating a new technical approach or instrument developed through support from the NCI Innovative Molecular Analysis Technologies (IMAT) program. Awards from this FOA are meant to incentivize independent validation and accelerate the adoption of these emerging technologies by appropriate research communities. Since its inception in 1998, the IMAT Program has focused on stimulating and accelerating the development, integration, and dissemination of highly innovative molecular and cellular analysis and biospecimen science technologies in support of cancer research and clinical care. Together with the NCI's other technology-focused programs, the IMAT program supports the development of tools and methods that enable new discoveries, enhance our understanding of cancer etiology and proliferation, improve detection capabilities, develop diagnostic methods and treatment strategies, conduct population-scale studies, address and reduce disparities in clinical care, and assist in clinical decision-making (recent FOAs include

RFA-CA-17-010

,

RFA-CA-17-011

,

RFA-CA-17-012

, and

RFA-CA-17-013

).

Due September 28, 2018

Funds available are 150K for one year.

Slide21

Germline mutations in ATM and KIAA0930 affect lung cancer risk with high effect

Xuemei Ji, Christopher I Amos

Slide22

Overview

Lung cancer is a leading cause of cancer death in the U.S. and around the worldIn the past decade, GWAS identified lung cancer susceptibility regions and several candidate common variants for lung cancer risk. Few previous studies investigated the association between germline mutations and lung cancer risk.

Although only less than 1% of most populations are carriers of a germline mutation that highly drives cancer, those carrying such germline mutations may have an 80% lifetime risk for developing cancer.

Slide23

Discover the driver germline mutations with high effect on lung cancer risk

 

 

 

 

 

 

ONCOARRAY

 

TRICL

SNP

CHR

BP

Gene

Function

 

F_A

F_U

P

OR

 

F_A

F_U

P

OR

rs56009889

11

108196896

ATM

Leu2307Phe

0.0028

0.0009

3.68E-08

3.05

0.0018

0.0010

0.16

1.83

rs150665432

22

45608215

KIAA0930

stop

 

0.0122

0.0048

5.72E-24

2.53

 

0.0048

0.0029

0.04

1.67

Association analyses for all the mutations (MAF < 0.01) within whole exome using the

OncoArray

cohort.

P < 5.0 x 10

-8

, OR > 2.0

Validated in

Tricl

cohort.

Slide24

Mutation in rs56009889 and rs150665432 results in protein change

Slide25

rs56009889 in ATM

Slide26

rs150665432 in KIAA0930

Slide27

Stratified analyses of rs56009889 in ATM

Slide28

rs56009889 highly affected lung adenocarcinoma risk, primarily in women and never smoker.

Slide29

rs150665432 in KIAA0930

highly affected the risk of lung cancer, mainly of small cell lung cancer

Slide30

Homozygotes of mutated allele

 

 

Control

 

Case

 

Crude

SNP

Allele

No.

%

 

No.

%

 

OR (95%CI)

P-value

rs56009889

GG

15414

99.82

18952

99.48

1.00

AG

28

0.18

93

0.49

2.70

1.77

4.12

4.14E-06

AA

0

0

6

0.03

Inf

0.96

Inf

0.04

 

Trend

 

 

 

 

 

 

 

 

 

5.79E-07

rs150665432

GG1501299.041794897.72AG1450.963882.112.241.852.712.10E-16AA10.01300.16

24.82

3.42

180.22

0.002

 

Trend

 

 

 

 

 

 

 

 

 

5.38E-13

Slide31

Dense imputationThe additional genetic variants in ATM can influence lung cancer risk in female and lung adenocarcinoma risk.

The additional genetic variants in KIAA0930 affect lung cancer risk.

Slide32

Summaryrs56009889 highly affected the risk of lung cancer, mainly of lung adenocarcinoma, primarily in women and never smoker.

rs150665432 highly affected the risk of lung cancer, mainly of small cell lung cancer, primarily in men and smoker.All homozygotes of mutated allele in rs56009889 are lung adenocarcinoma and 97% homozygotes of mutated allele in rs150665432 are lung cancer, supporting the both mutations affect lung cancer risk with high effect.In imputation, the additional genetic variants in ATM and KIAA0930 affect lung cancer risk, suggesting both ATM and KIAA0930 are associated with lung cancer etiology.

Slide33

Thank You

Slide34

Data Sharing for Oncoarray

Dartmouth

Current Approach

New Approach

Slide35

dbGAP Data Sharing -OncoArray

All sites funded by NIH funding must allow data sharingExcludes German StudiesNot applied to Copenhangen/Danish StudiesIncludes Chinese StudiesData must be uploaded to dbGAP but data use restrictions can be applied as can additional data access provisions (CARET, Korean studies)

Slide36

Affymetrix Studies - Caucasians

Characteristic and description

Cases

Controls

Total

N

(%)

N

(%)

N

(%)

Study

CARET

Nested case-control

USA

539

(34.7)

1013

(65.3)

1552

(14.5)

EPIC-Lung

Nested case-control

Europe (Multi-site)

1010

(49.6)

1025

(50.4)

2035

(19.0)

NHS/NPHS

Nested case-control

USA

519

(51.1)

496

(48.9)

1015

(9.5)

LCS

Hospital-based

USA

661

(46.9)

749

(53.1)

1410

(13.2)

LLP

Hospital-based

UK

395

(51.0)

380

(49.0)

775

(7.2)MECNested case-controlUSA206(49.6)209(50.4)415(3.9)MSH-PMHClinic-basedCanada1088(54.1)922(45.9)2010

(18.8)

PLCO

Nested case-control

USA

158

(30.7)

356

(69.3)

514

(4.8)

RUSS

Hospital-based

Russia

497

(51.0)

477

(49.0)

974

(9.1)

Histology

Adenocarcinoma

1

 

1816

(35.8)

-

-

1816

(35.8)

Squamous cell carcinoma

1

 

1274

(25.1)

-

-

1274

(25.1)

Other

1

 

1984

(39.1)

-

-

1984

(39.1)

Age

≤50 years

 

695

(42.4)

946

(57.6)

1641

(15.2)

>50 years

 

4376

(48.3)

4677

(51.7)

9053

(84.8)

Sex

Males

 

2902

(47.7)

3177

(52.3)

6079

(56.8)

Females

 

2170

(47.0)

2448

(53.0)

4618

(43.2)

Smoking

Non-smokers

 

515

(23.5)

1673

(76.5)

2188

(20.4)

Smokers

 

4558

(53.5)

3954

(46.5)

8512

(79.3)

Mean cigarette pack-years (SD)

 

37.2

(27.1)

22.9

(24.6)

29.7

(27.8)

Total

 

 

 

5073

(47.4)

5627

(52.6)

10700

(100.0)