The Sanger/TCGA (The Cancer Genome Atlas) stuff seems to be specific to microarray data which is different (older, more expensive) than the newer high-throughput data.

The figure you linked is a good explanation. The split read method is helpful for finding the edges of the CNV, while the number of reads (relative to other regions that were tested) can give an idea of the number of copies. The problem is that these methods all have their own unique biases/noise that makes it non-trivial to figure out the absolute copy number change.

Ideally they would find a similar CNV that has some clinical association.

The DGV has a lot of reference CNVs. Here are some in BRCA1: http://dgv.tcag.ca/gb2/gbrowse/dgv2_hg19/?name=id:3087443;db...