Loretta De Chiara

Universidad de Vigo, Centro de Investigaciones

Biomédicas - CINBIO, Vigo.

Deciphering novel non-invasive methylation

biomarkers for CRC screening in

serum circulating cell-free DNA

DNA methylation and cancer

Normal cell

Promoter hypermethylation

Transcriptional silencing

Global hypomethylation

Chromosomal instability

Tumor cell

Colorectal cancer

Features that define an AA: → ≥ 1cm in size → Villous hystological architecture → High grade dysplasia

PREVENTION EARLY DETECTION

Carcinoma Advanced adenoma

Non-advanced adenoma

Metastasis Normal epithelium

Hyperproliferative epithelium

Colorectal cancer screening

Fecal Immunochemical Test

Modest sensitivity for the detection of CRC and AA

Colonoscopy

Poor acceptance → 25%

Colorectal cancer screening

Fecal Immunochemical Test

Modest sensitivity for the detection of CRC and AA

Colonoscopy

Poor acceptance → 25%

CIRCULATING CELL-FREE DNA

(cfDNA)

cfDNA as a potential source of non-invasive methylation

biomarkers for CRC screening

Strategies for methylation biomarker discovery in liquid

biopsy

MLH1 ALX4

NEUROG1 SEPT9

TPEF/HPP1 HLTF

…

Genome-wide methylation analysis of serum cfDNA

Individual or combination of serum methylated biomarkers studied to date have not shown optimal sensitivity for AA detection and CRC screening

• Non-exhaustive selection, symptomatically limited study cohorts

AIM Identification of methylation biomarkers in serum cfDNA for the early detection of CRC and AA, to be implemented in population-

wide screening programs

• Epigenome-wide studies using tissue (tumor and healthy mucosa)

Methylation microarrays Candidate biomarkers selection

Screening cohort Average risk individuals

500 individuals

Biomarker validation

Sub-cohort proof-of-principle study

340 individuals

Multicentric cohort Classified according to colonoscopy result

Patients: serum samples

Discovery cohort

DNA pooling approach for biomarker discovery

Sham et al., Nature Reviews Genetics, 2002

Pearson et al., The American Journal of Human Genetics, 2007

Gallego-Fabrega et al. Clinical Epigenetics, 2015

Alternative to reduce costs in association studies

(GWAS / EWAS)

Highly significant correlations of methylation levels in individual samples and their corresponding pooled samples

Proof-of-principle study

The epigenome is mostly represented in pooled

serum cfDNA samples

>99% of detected CpGs

Gallardo-Gómez et al. Clinical Epigenetics, 2018

• 30 individuals – no colorectal findings (3 pools) • 50 individuals – benign pathologies (5 pools) • 50 individuals – non-advanced adenomas (5 pools)

130 cases – NO NEOPLASIA (NN) 150 cases – ADVANCED NEOPLASIA (AN)

Patients: Discovery cohort

• 50 patients – proximal advanced adenomas (5 pools) • 50 patients – distal/proximal advanced adenomas (5 pools) • 30 patients – CCR stage I/II (3 pools) • 20 pacients – CCR stage III/IV (2 pools)

Construction of pooled samples

cfDNA extracted from serum samples

Pooled samples: equal amount of cfDNA from 5 men and

5 women from the same pathological group, age- and recruitment hospital-

matched

DNA methylation analysis

Sodium Bisulfite modification of pooled cfDNA samples MethylationEPIC

BeadChip (Illumina)

> 850.000 CpG sites

• 99% of RefSeqGenes

• >95% of CpG Islands

• Coverage across gene bodies,

promoters, UTRs, introns, exons

Normalization Data correction

Raw data

Methylation values (%)

PRE-PROCESSING DIFFERENTIAL

METHYLATION ANALYSIS

BIOMARKER SELECTION

Differential analysis

Differentially methylated

positions (DMPs)

Feature selection

CpGs with high predictive value

Model Evaluation Cross-validation

Candidate Biomarkers

Bioinformatic processing and statistical analysis

minfi, missMethyl, sva (R/Bioconductor)

minfi, limma, qqman (R/Bioconductor)

Differential methylation patterns between no

neoplasia vs advanced neoplasia pools

326 Hyper

50 Hypo

Volcano plot

Manhattan plot

376 differentially methylated positions (DMPs) FDR 10%

Differential methylation analysis

Unsupervised clustering plot

5´ UTR TSS200

TSS1500 First exon

Distribution of DMPs relative to CpG islands

intergenic

intragenic

promoter

5´ UTR TSS200

TSS1500 First exon 3´ UTR Gene body

50 Hypomethylated DMPs

326 Hypermethylated DMPs

intergenic

intragenic

promoter

3´ UTR Gene body

64.3%

21.4%

62.6%

21.3%

5´ 3´

TSS 1500 First exon

TSS 200 5´UTR 3´UTR

Gene body

14.3% 16.1%

11.1% 16.7%

38.9%

33.3%

83.3%

16.7%

5.4%

37.9%

27.0%

29.7% 94.4%

5.6%

Statistically Equivalent Signature algorithm for feature selection

3,256 combinations of 118 CpG sites with statistically equivalent predictive value

Unsupervised clustering based on the 118 CpGs

Classification models based on those 3,256 signatures for selection of Top 20 CpGs

Selected CpG candidates for validation

No neoplasia Advanced neoplasia

The goal: Methylation Signature for CRC Screening

NN AN

Thank you

Collaborators:

Plan Nacional I+D+I 2015-2018 (Acción Estratégica en Salud)

Instituto de Salud Carlos III-FEDER (PI15/02007)

Ayudas FPU para la Formación de Profesorado

Universitario, MECD (FPU15/02350)

Molecular Biomarkers Group

Convocatoria Asociación Española Contra el Cáncer

Grupos Coordinados Estables de Investigación