Analysis of mutational spectra: locating hotspots and clusters of mutations using recursive segmentation

Computational methods for detecting cancer hotspots

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

Rare genetic variants and treatment response: sample size and analysis issues

Incorporating information about common genetic variants may help improve the design and analysis of clinical trials. For example, if genes impact response to treatment, one can pregenotype potential participants to screen out genetically determined nonresponders and substantially reduce the sample size and duration of a trial. Genetic associations with response to treatment are generally much larger than those observed for development of common diseases, as highlighted here by findings from genome-wide association studies. With the development and decreasing cost of next generation sequencing, more extensive genetic information - including rare variants - is becoming available on individuals treated with drugs and other therapies. We can use this information to evaluate whether rare variants impact treatment response. The sparseness of rare variants, however, raises issues of how the resulting data should be best analyzed. As shown here, simply evaluating the association between each rare variant and treatment response one-at-a-time will require enormous sample sizes. Combining the rare variants together can substantially reduce the required sample sizes, but require a number of assumptions about the similarity among the rare variants' effects on treatment response. We have developed an empirical approach for aggregating and analyzing rare variants that limit such assumptions and work well under a range of scenarios. Such analyses provide a valuable opportunity to more fully decipher the genomic basis of response to treatment.

Mutation hotspots in the p53 gene in tumors of different origin: correlation with evolutionary conservation and signs of positive selection

We present a classification analysis of the mutation spectra of the p53 gene and construct maps of hotspots for the germline (Li-Fraumein syndrome), different types of tumors and their derived cell lines. While spectra from solid tumors share common hotspots with the germline spectrum, they also contain unique sets of somatic hotspots that are not observed in the germline. All these hotspots correspond to amino acid replacements in the DNA-binding interface of p53. The mutation spectra of lymphomas and cell lines derived from lymphomas and lung cancers contained few hotspots compared to solid tumors. Thus, the distribution of hotspots in the p53 gene appears to depend on the tumor type and cell growth conditions; this specificity is missed by the bulk hotspot analysis. A negative correlation was detected between the amino acid replacement propensity in tumors and evolutionary variability: the hotspots are located in the positions that are highly conserved in p53 and its paralogs, p63 and p73. In all the mutation spectra, substitutions leading to amino acid replacements strongly dominate over silent substitutions, indicating that functional sites evolving under strong purifying selection are subject to intensive positive selection in p53-dependent tumors. These results are compatible with the gain-of-function concept of the role of p53 in tumorigenesis.