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Bioinformatic solutions for chromosomal copy number analysis in cancer
Scheinin, I.
2017
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Scheinin, I. (2017). Bioinformatic solutions for chromosomal copy number analysis in cancer.
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Contents
Abbreviations . . . 9 Abstract . . . 10 English . . . 10 Finnish . . . 11 Dutch . . . 12 1 Introduction 15 Chromosomal aberrations in cancer . . . 16Challenges for data analysis of CNAs . . . 17
Aberration length and magnitude . . . 17
Ploidy, cellularity, and heterogeneity . . . 18
Review of literature for data analysis of CNAs . . . 19
Microarrays for genome-wide CNA detection . . . 21
Array laboratory process . . . 21
Array data and meta-data . . . 21
Copy number analysis of microarray data . . . 22
Preprocessing of microarray data . . . 22
Segmentation and calling of microarray data . . . 25
Next-generation sequencing for CNA detection . . . 28
Sequencing laboratory process . . . 28
NGS data and meta-data . . . 29
Approaches for copy number analysis by NGS . . . 29
Paired-end mapping methods . . . 30
Split-read methods . . . 30
Depth of coverage methods . . . 31
Assembly-based methods . . . 31
Combinatorial methods . . . 32
Copy number analysis of DOC data . . . 32
Preprocessing of DOC data . . . 32
Segmentation and calling of DOC data . . . 33
Downstream analyses of CNAs . . . 36
Regioning to reduce dimensionality . . . 36
Identification of recurrent aberrations . . . 37
Statistical tests for association with clinical data . . . 37
Clustering for subtype discovery . . . 40
Aims of this dissertation . . . 42
2 CanGEM: mining gene copy number changes in cancer
Scheinin et al. (2008) Nucleic Acids Research 36: D830-D835 59
3 CGHpower: exploring sample size calculations for chromosomal copy
num-ber experiments
Scheinin et al. (2010) BMC Bioinformatics 11: 331–340 67
4 DNA copy number analysis of fresh and formalin-fixed specimens by
shal-low whole-genome sequencing with identification and exclusion of prob-lematic regions in the genome assembly
Scheinin and Sie et al. (2014) Genome Research 24: 2022–2032 79
5 Spatial and temporal evolution of distal 10q deletion, a prognostically un-favorable event in diffuse low-grade gliomas
van Thuijl and Scheinin et al. (2014) Genome Biology 15: 471–483 91
6 Summary and discussion 105
Summary of the original publications . . . 106
CanGEM database for CNAs in cancer . . . 106
Clinical data . . . 106
Copy number analysis of microarray data . . . 106
Sample size calculations with CGHpower . . . 107
Copy number analysis and power calculations . . . 107
Diagnostic plots . . . 108
Copy number preprocessing with QDNAseq . . . 108
Correction to read counts and identification of problematic regions in the genome . . . 108
Performance evaluation . . . 109
CNAs in low-grade gliomas . . . 109
Associations between CNAs and survival . . . 109
Evolving picture of glioma classification . . . 110
Discussion . . . 111
Academic software development . . . 111
Bioinformatics software developed for this dissertation . . . 112
Conclusions . . . 119
References . . . 120
Full list of publications . . . 127
Acknowledgments . . . 129
