Genetic predictors of lung cancer risk and progression

Some results and new proposals

Christopher Amos, Ph.D.Olga Gorlova, Ph.D.Ivan Gorlov, Ph.D.Konstantin DragnevScott Gerber, Ph.D.James Rigas, M.D.David Christiani, M.D, Sc.D.

Genetic Associations and Mechanisms in Oncology (GAME-ON):

Transdisciplinary Research in Cancer of

the

Lung (TRICL)

Chris Amos

Discovery, Biology, and Risk of Inherited

Variants in

Breast Cancer

(DRIVE)David Hunter

Elucidating Loci Involved in

Prostate Cancer Susceptibility

(ELLIPSE)Brian Henderson

Transdisciplinary Studies of Genetic

Variation in

Colorectal Cancer

(CORECT) Stephen Gruber

Follow-up of

Ovarian Cancer

Genetic Association and Interaction Studies (FOCI)Thomas Sellers

PHASE 1DISCOVERY

PHASE 2FUNCTIONAL

ANALYSIS

PHASE 3RISK ASSESMENT

Manhattan plot of all lung cancers from 1000 Genomes Imputation

TP63

hTERT

hMSH5BRCA2

CHRNA5

CHEK2

Associations of common mutations in BRCA2 with cancer in Iceland

Comparison of AD and SQ LC

(C) SQ

Adenocarcinoma Squamous Carcinoma

TP63

hTERT

hMSH5BRCA2

CHRNA5

CHEK2CDKN2

CHRNA5

RAD52CLPTM1L

Squamous Lung CancerBRCA2 CHEK2

BRCA2 CHEK2

GWAS-translation • Knockdown studies of CLPTM1L and TERT show loss

of CLPTM1L expression is necessary for lung cancer development in a kRAS knockout mouse

• Comprehensive promoter methylation studies of risk loci implicate epigenetic deregulation of most SNP-associated lung cancer loci including CHRNA3, CHRNB4 and TERT in lung cancer susceptibility• Genotype-methylation associations in lung tumor tissue for TERT and CHRNB4 • CHRNB4 promoter hypomethylation and CHRNA3 + TERT promoter

hypermethylation as well as methylation-expression correlations in tumor tissue• CHRNB4 knockdown leads to reduced proliferation and propensity to form

colonies

Other Genetic Analysis Projects

Custom Affymetrix Array• 9 studies concentrating on

cohorts– 7,500 lung cancer cases– 7,500 controlsCustom Array with 300,000 exome array markers100,000 custom markers including markers derived from sequencing studies and pharmacogenetic variants

Exome plus targeted regions sequencing• Sequencing of 1000 lung

ca. cases and 1000 contols• Funded through a separate

application to CIDR• Includes samples from the

Custom Affymetrix Array Study to inform imputations

• Selecting early onset cases, family history positive, cases with tumor samples and rare variant carriers

GAME-ON OncoArray

OncoChip600K

beadtypes

GWAS Backbone260K

Illumina Core

Common Content – 40K Fine-mapping of common cancer susceptibility loci (TERT, 8q24 (proximal and

distal to MYC), HNF1B, TET2, RAD51B, 11q13, MERIT40, MDM4)Ancestry Informative Markers

Cross-Site meta analysisPharmacogenetic components

eQTL (Height, Weight, BMI, WHR, Menarche, Menopause etc)Other cancers published GWAS variants

Chromosome X and mitochondrial DNA variants

Cancer Specific Variants

Lung Colon Breast

ProstateOvarian

(proportional allocation)

Proposed Research Studies

• Shared decision making and tumor analysis– Proposed application of 100 lung cancer cases

with hotspot mutation versus exome sequencing • Collaboration between Karmanos Cancer

Institute and Dartmouth• Reviewers liked Dartmouth component but

not Karmanos – lack of electronic medical record at KCI, insufficient process details

Predicting Risk for Recurrence

• Proposed collaboration to Lungevity Foundation

• Uses snap frozen samples from Harvard to perform integrated analysis – genomic mutations and proteomic alternations

• 200 cases selected for recurrence or nonrecurrence

• Could be extended in R01 to larger sample size• Extend to other lung cancer phenotypes

U01 Grant On Integration of SNP Data in Lung Cancer Screening

• In collaboration with Dr. Kimmel from Rice we are working on the proposal to integrate GWAS-detected risk and outcome SNP into lung cancer screening model.

• As the first step we will estimate effects of SNPs on tumor growth and metastasizing rate. We will use NLST and TCGA data.

• We will then incorporate SNPs into the model of natural history of lung cancer with the screening module superimposed onto it.

• SNPs in the model will be incorporated based on their frequency and estimated effect size on tumor growth and metastasizing rates.

• The goal is to estimate if targeted genotyping of the risk associated SNPs will improve screening efficacy.

P01 Integrative Analysis of Lung Cancer Risk

Biostatistics and QC Core

Project 1Smoking

Genetic PredictorsDependence

Project 2: Genomic and Epigenetic

Predictors of Risk

Project 3: Intermediate

predictors of risk: miRNA,

metabolomiic and ‘nutritional’ exposures

Project 4:Application of Risk

Models to Screening

Populations

Genomics and Genetics Core

Genetic Mapping of DNA Methylation in EAGLE Lung

Conducted methylation quantitative trait loci (QTL) analysis of EAGLE normal lung tissues in 210 samples, with 450K CpG probes, replicated in TCGA lung tissue(Additive model between each SNP and normalized methylation trait pair, adjusting

for sex, age, plate, population stratification and methylation-based PCA scores)

Shi et al., Nature Communications (In press)

34,304 cis-meQTL (mapping to 9,963 genes)

585 trans-meQTLs

Most meQTLs are not in gene promoters or CpG islands

cis region=500kbtrans region>500kb or different chromosomes

cis-meQTL in lung cancer GWAS loci

15q255p15

12p13 9p21

CHRNA5

CHRNA3

TERT CLPM1L

RAD52 CDKN2A

6p21

MSH5

Inherited genetic variation may affect lung carcinogenesis by regulating the human methylome