Methylation Makers for D Detection of Endometrial i f E d i l - - PowerPoint PPT Presentation

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Methylation Makers for D Detection of Endometrial i f E d i l - - PowerPoint PPT Presentation

Methylation Makers for D Detection of Endometrial i f E d i l Carcinoma Carcinoma Nicolas Wentzensen M.D., Ph.D., M.S. Senior Investigator Hormonal and Reproductive Epidemiology Branch p p gy Division of Cancer Epidemiology and


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Methylation Makers for D i f E d i l Detection of Endometrial Carcinoma Carcinoma

Nicolas Wentzensen M.D., Ph.D., M.S. Senior Investigator Hormonal and Reproductive Epidemiology Branch p p gy Division of Cancer Epidemiology and Genetics Advances in Endometrial Cancer Epidemiology and Biology Advances in Endometrial Cancer Epidemiology and Biology Boston, March 17-18, 2014

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Endometrial cancer survival

  • Good prognosis when detected early, but 25% of cancers

present at advanced stage and have poor survival

  • About 80% of endometrial cancer deaths occur due to

advanced stage type 1 cancers

Adenocarcinoma of the Corpus Uteri: Relative Survival Rate (%) by AJCC Stage (SEER modified 3rd edition), Ages 20+, 12 SEER Areas, 1988-2001

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Promise of early detection y

  • Most common gynecological cancer in the US:

49,560 cases and 8,190 deaths in 2013

  • Well defined high risk populations: Women

g p p with high BMI, postmenopausal bleeding, endometrial hyperplasia yp p

  • 1-2 million office visits each year in the US

related to postmenopausal bleeding related to postmenopausal bleeding

  • No uniform management of women at

increased risk of endometrial cancer

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Methylation profiling of endometrial cancers

Polish Endometrial Cancer Case- Benign Reproductive Tissue Study I:

Discovery

Control Study: 148 cancers 25 normal tissues Endometrial Hyperplasia Study: 69 cancers Benign Reproductive Tissue Study II: 43 normal tissues

Replication

  • Endometrial tissues from three population-based studies

DNA was extracted from paraffin embedded tissue

  • DNA was extracted from paraffin-embedded tissue
  • Methylation analysis on Illumina Golden Gate platform, covering

>800 cancer related genes g

  • Finding markers of etiologic heterogeneity and for early detection
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Normal endometrium Endometrial carcinomas

High % of MSI Low % of MSI

Methylation patterns show etiologic heterogeneity

High % of MSI Low % of MSI

Samples in columns Genes in rows Red: higher methylation 25% most variable probes

  • Two major cancer clusters: One cluster with high prevalence of microsatellite

instability (MSI)

  • Comparison of cancer and normal tissue: Over 300 sites with p<0.001; PTEN

pathway was most significantly methylated

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Genes with different methylation levels between endometrial cancer and normal endometrial tissue endometrial cancer and normal endometrial tissue

Methylation y Gene control case difference p-value ASCL2 0.12 0.78 0.66 < 10-7 HTR1B 0.10 0.75 0.65 < 10-7 HS3ST2 0.19 0.78 0.59 < 10-7 SOX1 0.20 0.76 0.56 < 10-7 MME 0.06 0.61 0.55 < 10-7 ADCYAP1 0.09 0.61 0.52 < 10-7 NPY 0.17 0.68 0.51 < 10-7 CDH13 0.14 0.54 0.40 1.8x10-6

  • Top eight candidate genes with high methylation differences, low methylation in

controls (probes averaged for each gene) controls (probes averaged for each gene)

Wentzensen et al. IJC in press

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Replication of eight candidate genes in the validation study

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Prediction of endometrial carcinomas using methylation markers methylation markers

  • Two classifiers based on (1) top 8 genes and (2) all 800 genes included on

the array were successfully replicated in independent samples

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Replication of methylation markers in TCGA

Endometrioid Carcinomas Serous Carcinomas

Gene Normal tissues (n=43) Cases (n=358) p-value AUC Cases (n=86) p-value AUC

ADCYAP1

0 07 0 60 0 0001 0 97 0 28 0 0001 0 77

ADCYAP1

0.07 0.60 <0.0001 0.97 0.28 <0.0001 0.77

ASCL2

0.12 0.55 0.0001 0.79 0.08 0.28 0.44

CDH13

0.16 0.54 <0.0001 0.95 0.18 0.83 0.51

HS3ST2

0 12 0 55 <0 0001 0 95 0 24 0 0005 0 69

HS3ST2

0.12 0.55 <0.0001 0.95 0.24 0.0005 0.69

HTR1B

0.12 0.56 <0.0001 0.99 0.27 0.0001 0.96

MME

0.20 0.48 <0.0001 0.85 0.22 0.19 0.57

NPY

0 10 0 57 <0 0001 0 92 0 20 0 02 0 63

NPY

0.10 0.57 <0.0001 0.92 0.20 0.02 0.63

SOX1

0.14 0.60 <0.0001 0.99 0.45 <0.0001 0.96

  • All eight markers replicate in endometrioid cancers from TCGA
  • All eight markers replicate in endometrioid cancers from TCGA
  • Four (to five) markers replicate in serous cancers from TCGA
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Evaluation of candidate markers in lower genital t t l tract samples

1. Tao brush: Sampling from 2. Tampon: Collecting blood and complete uterine epithelium discharge from the vaginal pool

  • Pilot study with Mayo Clinic: Collection of lower genital tract

samples from 40 women with cancer and 40 women without cancer

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Replication of candidates in lower genital tract specimens specimens

Odds ratio

5/5 did t f ll li t d i T b h l

Wentzensen et al. IJC in press; Bakkum-Gamez, Wentzensen et al. submitted

  • 5/5 candidates were successfully replicated in Tao brush samples
  • 4/5 candidates were successfully replicated in Tampon samples
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NPY methylation in cases and controls

80

NPY

60 40 20

Primary EC Tao Brush EC Tampon EC Tao Brush Control Tampon Control

  • Pos. 1 Methylation (%)
  • Pos. 2 Methylation (%)
  • Increasing dilution of methylation signal
  • Good discrimination of case-control status
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Performance of markers in Tao brush samples

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Performance of the combined analysis of 11 CpG sites in Tampon samples 11 CpG sites in Tampon samples

  • The AUC of the combined model is 0.85
  • At a cutoff of 1 or more hypermethylated sites, the assay has 83%

sensitivity and 83% specificity

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How can the risk stratification of methylation markers be used clinically?

  • Refer women to treatment

Refer women to treatment

  • Refer women for further diagnostic

evaluation

  • Reassure women that no further
  • Reassure women that no further

evaluation is necessary

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A study to evaluate methylation markers for detection of endometrial carcinoma

  • 1,000 women 45 years or older presenting at Mayo Clinic

– Evaluation of abnormal endometrial bleeding, discharge, thickening of endometrial stripe

  • At least 5% estimated prevalence for atypical hyperplasia

ll as well as cancer

  • Collection of Tampon and Tao brush samples, blood,

tissue from endometrial biopsies and surgery RF data tissue from endometrial biopsies and surgery, RF data

  • 2-year follow-up of women without endometrial cancer
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Risk stratification for prediction of endometrial cancer cancer

40% 100% Risk of endometrial cancer Methylation + 40% Methylation - 10%

Women at increased risk

  • f endometrial cancer

Methylation 0% 2%

  • Possibility of self-sampling
  • Integration of clinical symptoms and methylation markers into risk prediction

models models

  • Evaluation of methylation markers in endometrial hyperplasia

Wentzensen and Wacholder Cancer Discovery 2013

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Collaborators

Magee Women’s Hospital

Collaborators

NCI-DCEG Clara Bodelon Louise Brinton Mayo Clinic Jamie Bakkum Lori d’Ambrosio Richard Guido Ashley Felix Mark Greene Patricia Luhn Jamie Bakkum Karl Podratz Viji Sridhar Cancer Center, Warsaw Jolanta Lissowska Ruth Pfeiffer Joshua Sampson Hannah Yang NCI-CCR Stephen Hewitt Keith Killian Kaiser Permanente Northwest Andrew Glass Kathy Pearson NCI-DCP Mark Sherman Keith Killian Paul Meltzer Brenda Rush City of Hope Jim Lacey