simposio sobre las realidades de la biopsia líquida - deciphering … · 2019-01-30 · loretta de...
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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