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Page 2 of 14 Gottlieb et al. J Cancer Metastasis Treat 2018;4:37 I http://dx.doi.org/10.20517/2394-4722.2018.26

Keywords: Intra-tumor genetic heterogeneity, breast cancer, complex single gene variance

INTRODUCTION
Current carcinogenesis hypotheses
The traditional understanding of carcinogenesis, that cancer cells accumulate somatic driver mutations that
[1]
give them a growth advantage is beginning to be questioned as data reveal the presence of driver gene
[2]
mutations involved in carcinogenesis in normal tissues . Further, a critical issue still to be elucidated is how
these mutations create a gain-of-function in cells that results in them acquiring new oncogenic properties,
rather than just the loss-of-function of factors that control cell growth and division. One indication as to
why these properties might be more complicated than a simple case of excessive or distorted growth is that
[3]
cancer genes are generally not over-expressed in the tissues from which the cancer develops . For example,
out of 130 highly specific-cancer genes only four are most highly expressed in the tissue from which the
[3]
cancer originates . Thus, other factors besides protein accumulation are likely to be involved. Compound-
ing this conundrum is the observation that there are often different mutations in different cancer-associated
[1]
genes in different cancer tissues . Raising the question as to how these differences are related to the tissue
specificity of certain cancer mutations.

Further, in a recent study looking for associations between specific cancer genes and specific cancer tis-
[1]
sues some genes did not behave as expected . The analyses suggested that both cell-intrinsic (i.e., genomic
and epigenetic) and cell-extrinsic (i.e., environmental, both internal and external) factors could explain the
differences in the cell type-specificity of cancer genes. For example, in breast cancer, specific external en-
[4]
vironmental factors have included estrogen receptor alpha (ER) activation by estradiol and conversion of
[5]
estrogen into genotoxic metabolites that can cause DNA double-strand breaks . However, in most cases it
has not been possible to associate any specific intrinsic or extrinsic factor with cancer tissue specificity. Un-
derlying these fundamental questions is a growing awareness of substantial amounts of genetic heterogeneity
[6]
not only within different types of cancer tissues , but within single tumor cancer tissues as well. These latter
[7]
observations have been labelled as intra-tumor genetic heterogeneity (ITGH) .
Intra-tumor genetic heterogeneity
ITGH identified within breast tumors, has revealed numerous alterations in different genes, with the as-
[8]
sumption that most mutations are in “passenger” genes , including studies using single cell sequencing
[9]
techniques . However, such studies have also not drawn many definitive conclusions as to precise roles of
many of the “driver” genes in carcinogenesis. Genes being identified as drivers: (1) if they are either onco-
genes or tumor suppressor genes; (2) if they function in some aspect of cell growth; (3) if their location are
[10]
close to any of these types of genes . Further, a recent paper noted that passenger genes can also have dam-
[11]
aging effects on cancer progression .

We believe this confusion is partly because of a failure to investigate the nature and degree of genetic het-
erogeneity within single genes, a condition that we have labelled, complex single gene variance (CSGV), as
opposed to just identifying mutations in different cancer-associated genes. Why this is important is that as
[12]
natural selection is being increasingly identified as a critical process in cancer biology , there needs to be a
better understanding of the nature of the genetic variation that is being subjected to selection.

Identification of single gene genetic heterogeneity
The question as to why genetic heterogeneity within individual genes has not been studied before is partially
because the approach to identifying gene variants is based on using sequence analysis algorithms and tools
that make it inherently difficult to identify CSGV. Essentially, they are designed to ignore or minimize the