Limitations and future directions

Some aspects within this thesis could be further improved by future work. Although the coverages of 600 TCGA WGS data used in this study are sufficient for SV and SCNA analy- sis, much higher coverages are need to confidently call somatic SNVs, thus somatic SNVs are not investigated in this thesis. It is interesting to explore the relationship between the fea- ture of SVs in cancer genome and behavior of somatic SNVs. For example, TP53 mutations have been revealed to be highly correlated with SCNAs in various tumor types (Fridlyand et al., 2006; Snijders et al., 2005). A previous study on HNSC showed that TP53 mutation is frequently accompanied by loss of chromosome 3p and the combination of these events is associated with a surprising decrease in survival time (Gross et al., 2014).

The presence of variant subclones within the cancer cell population, termed as intratu- mor heterogeneity, is another feature of primary tumor genomes, and brought tremendous challenges in bioinformatics analysis of tumor WGS data (Vogelstein et al., 2013). For example, the existence of subclones in WGS data will confound the identification of SVs and estimation of ASCNS/ASCNG. Studies have revealed subclonal changes across distinct geographic regions of a primary tumor (Gerlinger et al., 2012) and within hematopoietic malignant populations (Ding et al., 2012). A multiregion sequencing study on LUAD re- vealed that a larger subclonal mutation fraction may be associated with increased likelihood of postsurgical relapse in patients (Zhang et al., 2014). It has been proposed that the major cause of genetic heterogeneity in cancer is genomic instability, including BFB (Gisselsson et al., 2000), which leads to an increased mutation rate and can shape the evolution of the cancer genome (Burrell et al., 2013). The development of single-cell sequencing technology has accelerated studies of cancer heterogeneity (Navin et al., 2011; Eberwine et al., 2014; Eirew et al., 2014). Single-cell sequencing is now providing cutting-edge clinical applica- tions, especially in studying blood-borne circulating tumor cells (CTCs) (Heitzer et al., 2013; Ni et al., 2013; Swennenhuis et al., 2013), derived from a solid tumor, to investigate the value of CTCs for guiding diagnosis, prognosis, and treatment of the cancer. However, the amount of DNA in single cell is not enough for current NGS technology and whole genome amplification, which has inherent risk of inducing significant bias, is inevitable (Tre↵ et al., 2011; Alix-Panabi`eres and Pantel, 2014). Moreover, the isolation of single cell from tissues and designing an optimal single-cell sampling strategy, which can comprehensively represent the mutational landscapes within a primary tumor are both challenging (Eberwine et al.,

2014). There are still pressing needs for building computational models which can tackle the heterogeneity in primary tumor sequencing data.

Although the length is growing, NGS reads are still too short to cover larger or more complex structural alterations, especially those with breakpoints inside centromere or other low sequence complexity regions. Indeed, many arm-level SCNAs might have their break- points right inside centromere gap regions and the SVs which formed these SCNAs would inevitably be missed. Hybrid approaches that combine OM or ‘third generation sequenc- ing’ provided by PacBio, together with NGS, might provide new insight into those genomic regions hard to explore by current NGS technology alone.

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