Table 1 Explanations of key terminologies

Aneuploidy is a changed genomic state with an abnormal number of chromosomes in a cell. In cancer, most aneuploidy is not clonal or constitutional. Recently, a looser definition of aneuploidy has been used to analyze DNA sequence data, which includes partial chromosomal changes and somatic copy number aberration (SCNA). Such usage is not precise, as germline CNVs and SCNA represents the variable copy number of specific sequences, which is not the same as the entire abnormal chromosome(s). According to the genome theory, the chromosome represents a coding system, so the impact of aneuploidy is therefore much more significant than SCNA. The mechanisms causing somatic aneuploidy are many; examples can be found in Table 2.

CIN: Chromosome instability (CIN) refers the rate (cell-to-cell variability) of changed karyotypes of a given cell population. There are two types of CIN: numerical and structural. Numerical CIN is determined by the gain or loss of whole chromosomes or fractions of chromosomes (aneuploidy), as well as other forms. Structural CIN, on the other hand, is determined by structural NCCAs. Numerical and structural CIN often co-exist. CIN can be effectively measured by the frequency and type of NCCAs.

Type I and type II CIN: CIN can be classified into two types based on its involved molecular mechanisms. Type I CIN is directly linked to the maintenance of genome integrity within the chromosomal cycle, including the chromosomal machinery, checkpoints, and repair systems (see Table 3). Type I CIN is often detected in chromosome instability syndromes which provide good examples of direct “molecular causative relationship” between identified genes and CIN. However, mutations to type I genes are rare and they do not explain sporadic cancer. In contrast, the mechanisms of type II CIN are often associated with non-genetic factors such as the micro-environment and physiological processes, which do not have a direct molecular causative explanation. The diverse type II mechanisms all share one common feature: they are involved in the cellular system’s response to stress, increasing heritable changes [50].

Fuzzy inheritance: In contrast to the gene theory, which states that a gene codes for a specific, fixed phenotype, the genome theory suggests that most genes code for a range of potential phenotypes. From this “fuzzy” range of phenotypes, the respective environment can then allow the best-suited status to be “chosen” [4, 37, 59]. For example, the gene for pea color codes for an entire potential spectrum of colors, from yellow to intense green (including blends of yellow and green, or green with yellow spots), not just two fixed, distinctive colors (yellow or intense green). In cancer, the emergence of “genomic context” adds yet another layer of complexity and instability that pushes fuzzy inheritance’s dynamics to a maximal status.

Macro-and micro-cellular evolution: Macro-cellular evolution refers to karyotype change-mediated somatic cell evolution, which alters the genome context of a given cellular system. In contrast, micro-cellular evolution refers to gene/epigene change-mediated evolution, which modifies a given cellular system within the same karyotype. Macroevolution and microevolution respectively refer to organismal evolution at the above-species level and at the population level within a species.

System inheritance: Unlike the gene-defined “parts inheritance” (the instructions for making a given protein or RNA), a new three-dimensional genomic topologic coding, or the blueprint of the genome, is defined by the order of genes or other DNA sequences along and among the chromosomes of a given genome. This blueprint encodes how genes interact as an emergent property, which provides the instructions for how genomic networks work [4, 37, 66].