Structural variant detection in colorectal cancer E van den Broek 1 - - PowerPoint PPT Presentation

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structural variant detection in colorectal cancer
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Structural variant detection in colorectal cancer E van den Broek 1 - - PowerPoint PPT Presentation

May 19, 2023 302 likes •558 views

Structural variant detection in colorectal cancer E van den Broek 1 , JC Haan 1 , MH Jansen 1 , B Carvalho 1 , MA van de Wiel 2 , ID Nagtegaal 3 , CJA Punt 4 , B Ylstra 1 , S Abeln 5 , GA Meijer 1 , RJA Fijneman 1 1 Dept. of Pathology, VU

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Structural variant detection in colorectal cancer

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1

  • Dept. of Pathology, VU University Medical Center, Amsterdam, The Netherlands

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  • Dept. of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands

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  • Dept. of Pathology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

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  • Dept. of Medical Oncology, Academic Medical Center, University of Amsterdam, The Netherlands

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  • Dept. of Computer Science, VU University, Amsterdam, The Netherlands

E van den Broek1, JC Haan1, MH Jansen1, B Carvalho1, MA van de Wiel2, ID Nagtegaal3, CJA Punt4, B Ylstra1, S Abeln5, GA Meijer1, RJA Fijneman1

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SLIDE 2

Colorectal cancer (CRC)

  • Colorectal cancer is a major health concern worldwide
  • Second cause of cancer related death

– The incidence worldwide is 1,200,000 – The incidence in the US is 144,000

  • Mortality rates

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Stage 1 < 10 % Stage 2 25 - 30 % Stage 3 45 – 50 % Stage 4 > 90 %

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Diagnostic biomarkers Predictive biomarkers Prognostic biomarkers Clinical needs:

  • 1. Screening
  • 2. Predict recurrence
  • 3. Personalized therapy

CRC research

Clinical needs for biomarker discovery

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normal colon progressive adenoma adenoma localized CRC CRC liver metastasis CRC lymph node metastasis activated Wnt signaling genomic instability (~5% of adenomas) ~15% MIN+ ~85% CIN+ ~3% MIN+ ~97% CIN+ Key molecular features

20 40 60 80 100

5-year survival rate (%) Stage IV Stage I+II Stage III CRC stage:

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Chromosomal Instability

a hallmark of CRC

SKY: numerical & structural aberrations

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M Hermsen et al., Oncogene 2005

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CAIRO & CAIRO2 studies

Phase III clinical trials In total 1575 patients were included CApecitabine, IRinotecan, Oxaliplatin in advanced colorectal cancer CAIRO: Koopman et al. Lancet 2007 CAIRO2: Tol et al. N Engl J Med 2009 DNA from 356 patients: primary tumor and matched normal – Representative group – Isolated from FFPE

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Array CGH: 356 CAIRO & CAIRO2 samples

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Calling Copy numbers Segmentation

1 2 3

Numerical aberrations

Comparative Genomic Hybridization (CGH)

Agilent, 180k array CGH

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Segmentation - array CGH

Profile of one tumor with 180k probes

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Segmentation was performed using Circular Binary Segmentation algorithm

(DNAcopy. Olshen et al. 2004)

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Calling - array CGH

Profile of one tumor with 180k probes

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Calling was performed using CGHcall

(CGHcall. vd Wiel et al. 2007)

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Structural Variants (SV) in cancer

Hematological disorders

  • Philadelphia chromosome

– t(9;22) – Fusion gene: BCR-ABL – Drug: Imatinib / Gleevec Epithelial cancers

  • TMPRSS2-ERG in prostate cancers
  • VTI1A-TCF7L2 is confirmed in 3% of 97 CRCs

– Bass et al., Nature Genetics 2011

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TO IDENTIFY RECURRENT SOMATIC STRUCTURAL GENOMIC VARIANTS THAT CAUSE CRC

AIM

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Array CGH: 356 CAIRO & CAIRO2 samples

Breakpoint (BP) detection

Based on array CGH

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Breakpoint detection Candidate genes Segmentation

1 2 3

Structural variants

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BP detection in array CGH

Profile of one tumor with 180k probes

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Breakpoints are defined by the start position of the first probe of each segment Breakpoint annotation per gene

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  • Total number of genes with BPs: 5,737 genes
  • 482 candidate genes were identified with recurrent BP (FDR < 0.1)

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Results based on array CGH

BP detection

20 40 60 80 100 120 140

Amount of affected samples in array CGH

Candidate genes (top 15) MACROD2

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SLIDE 14

Overall survival: MACROD2

Recurrent BP (1) versus no-BP (0)

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Log rank P= 0.08

500 1000 1500 0.0 0.2 0.4 0.6 0.8 1.0

MACROD2

Overall Survival (days) Survival probability BP (samples) 0 ( 207 ) 1 ( 144 )

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  • Total number of genes with BPs: 5,737 genes
  • 482 candidate genes were identified with recurrent BP (FDR < 0.1)

Limitations breakpoint determination using array CGH: – Location BP is estimation (average probe distance is ~17 kb) – DNA structure is unknown – Balanced events will be missed

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CGH 482 CANDIDATE GENES

Results based on array CGH

BP detection

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  • 482 candidate genes were identified with recurrent BP (FDR < 0.1)

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CGH 482 CANDIDATE GENES Candidate validation is required

Validation array CGH BPs

NGS data from TCGA

The Cancer Genome Atlas CRC samples (COAD & READ) Whole Genome DNA Seq from paired tumor-normal samples

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SLIDE 17
  • 482 candidate genes were identified with recurrent BP (FDR < 0.1)

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CGH 482 CANDIDATE GENES Candidate validation is required

Validation array CGH BPs

NGS data from TCGA

Structural Variant (SV) detection Candidate driven Negative Control Genes (no BP)

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Computational methods

Focus on candidate genes

Based on paired-end NGS data

  • Read-pair approach

– Discordance: location / bridge length / orientation reads Discordant pairs (DP) types

  • Translocation

> different chromosomes

  • Insertion

> bridge length

  • Deletion

> bridge length

  • Inversion

> orientation

  • Eversion

> orientation

  • Single mapped

could indicate a breakpoint

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ref

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Computational methods

Focus on candidate genes

Based on paired-end NGS data

  • 1. Read-pair approach

Combined with:

  • 2. Read-depth
  • 3. Define breakpoint location
  • 4. Determine tumor specific events

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ref

1 2 3

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Translocation

IGV

MACROD2

  • Discordant pairs
  • Breakpoints

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!

Fusion partner

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Based on DP groups: Approximately 5 fold higher number of translocation-DP groups for candidate genes compared to control genes

Distribution DP groups per type

Preliminary results candidate genes in TCGA data

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Deletion 50.8% Eversion 8.4% Inversion 6.7% Insertion 26.4% Translocation 7.7%

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Translocation-DP groups per candidate gene in TCGA samples

Putative translocations

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Frequency of translocation-DP groups (au) Candidate genes Frequency of translocation-DP groups (au) Candidate genes (top 20) MACROD2

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SLIDE 23

0% 10% 20% 30% 40%

Correlation per candidate gene

  • Frequency of samples with BP based on array CGH
  • Frequency of translocation-DP groups in TCGA data

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Frequency of affected samples in array CGH analysis Frequency of translocation-DP groups (au) MACROD2

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Conclusions

  • 482 candidate genes with recurrent breakpoints were identified

in a large cohort of 356 CRC samples, based on array CGH analysis

  • The TCGA provided an essential CRC

reference dataset (COAD, READ) to validate Structural Variants in candidate genes with recurrent breakpoints

  • Identification of BPs based on array CGH is correlated with SV

detection in TCGA CRC NGS data

  • Further studies will be performed to investigate clinical and

functional significance of validated candidate genes

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