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During cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by $1$ or $-1$, even though these events can affect large portions of a chromosome. We propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor $2$ approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called \textsf{cnp2cnp}. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by \textsf{cnp2cnp} and compare it against two other alternatives, namely the \textsf{MEDICC} distance and the Euclidean distance. The experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the \textsf{MEDICC} distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the \textsf{MEDICC} approach should preferred over the Euclidean distance.