Tumour karyotype

1
Tumour karyotype
Spectral karyotyping showing chromosomal
aberrations in cancer cell lines
2
Chromosomal aberrations

• Segments of DNA that get duplicated
• Gains
• Segments of DNA that get deleted
• Losses
• Chromosomal aberrations are being investigated as
  diagnostic indicators of cancer and other
  diseases
• Better diagnosis of disease
• Potentially reveals biomolecular mechanisms of
  disease
• This research is done using
• array comparative genomic hybridization (aCGH)
• Measures DNA copy number changes

3
Array CGH

• Array CGH is a genetic technique used to identify
  chromosomal aberrations in cancer
• High resolution
• Full coverage

4
Array CGH data

• Measures log2 ratios of normal vs sample for
  pre-specified segments of the genome called
  clones
• Theoretical log2 ratios
• 1 copy gain (duplication) log(3/2) 0.58
• Neutral log (2/2) 0
• 1 copy loss (deletion) log(1/2) -1
• Measurement based on detection of hybridization
  level to probes on an array
• 30,000 measurements per sample

5
Computational challenges

• Noisy signals
• Spatial dependence between adjacent clones
• Outliers
• Systematic errors
• Copy number polymorphisms

6
Our approach

• Use a supervised hidden Markov model (HMM) to
  model spatial dependency between clones
• States are loss, neutral, gain-one, gain-many
• Infer the unobserved state sequence from the data
  using a standard efficient algorithm called for
  forwards-backwards
• This part of the model is standard
• Use a Gaussian mixture model to model the
  outliers separately from the inliers
• Inliers have spatial dependence
• Outliers do not
• Use prior knowledge about locations of CNPs to
  inform the model about possible locations of
  outliers
• Several published lists of CNPs are available
• Internally generated list more comprehensive
• Pool data across chromosomes to gain statistical
  strength

7
(No Transcript)
8
Test data

• Mantle cell lymphoma cell lines with ground
  truth

• 123 losses and 72 gains covering approximately 1
  of the human genome
• Compare results to state-of-the-art algorithms

9
Results
LSP-HMM-C had 97 recall and 83 precision
10
Example results
LSP-HMM
11
Example results
MERGELEVELS
Base-HMM
12
Conclusions

• HMM framework superior to MergeLevels
• Adding robustness further improves performance
  over the Base-HMM
• Adding LSP information improves performance
  marginally over robust HMM, but importantly does
  not make results worse
• Motivation for using more comprehensive lists to
  improve results
• Some false positives are they real?
• Pooling across chromosomes results in big gains
• Data more easily overwhelms incorrect priors
• Makes the algorithm less sensitive to parameter
  settings