With next generation sequencing (NGS) comes the power to
change medicine, to personalize patient treatment, to improve patient
outcomes. But before we become
overly optimistic, recall what we learned in our last discussion – that use of
NGS in the clinic possesses some limiting factors, one of which is our
incomplete understanding of cancer.
For personalized medicine to be effective, cancer scientists need to
have an in-depth understanding of the biology behind cancer. For those of us toiling away in the
research field, utilizing NGS has become a new tool for deciphering intricate
cancer networks.
In today’s discussion, we’ll uncover how a group of
researchers based in Portland Oregon used NGS to assess the RNA levels or
transcript levels of genes controlled by the tumor-promoting oncogene c-MYC in
a breast cancer cell line (for an explanation of the relationship between RNA
and DNA, look here). In this
report, the use of RNAseq (the NGS technology used to looked at RNA levels) composes
only a small, though critical, fraction of the overall study. It also represents the utility of NGS
technology in basic research.
In this article the authors built on their previous work
assessing the
stability of c-MYC. This
paper delves into the nitty-gritty of specific protein interactions and their
effects at the functional level. To set the stage, let’s introduce the key players. First of all, the main actor is c-MYC,
a transcription that induces transcription and expression of numerous
pro-survival keys. PP2A is a
phosphatase that alters c-MYC’s appearance (phosphorylation) to surrounding
proteins effectively leading to c-MYC degradation and loss of its
activity. SET and CIP2A are new to
me and new to the c-MYC story.
They inhibit PP2A by binding and preventing its interaction with
c-MYC. In this way, these two
proteins act as activators of c-MYC.
See Figure 1.
When activated, SET and CIP2A enhance c-MYC activity and promote
a pro-growth, pro-tumorigenic environment. In this study, the researchers show that decreased
expression of these two genes independently decreases the tumorigenic potential
of cells, both reducing proliferation and slowing tumor growth in mice. As an alternative to decreasing SET
expression, the use of a drug that targets and inhibits SET generates a similar
effect: c-MYC activity decreased and tumor progression was slowed. This type of data has implications into
new treatment options, in particular, as an indirect way to target c-MYC and
its pleiotropic effects on tumorigenesis.
Why are we talking about this paper? Because they used next
generation sequencing. Apart from
the mechanistic details outlined in this paper describing how SET and CIP2A
activate c-MYC, this article also looks on the overall scheme in which SET and
CIP2A are expressed using NGS. In
this context, the NGS technology used was specifically aimed at the RNA level –
called RNAseq. Recall that genes (DNA)
code for RNA (in a process called transcription – the function of c-MYC), which
code for protein. Because mRNA and
protein are translated in a one-to-one ratio, the level of mRNA is directly
proportional to the level of protein expression. In this study, the authors used RNAseq to assess the levels
of SET and CIP2A expression in breast cancer cell lines. They found that both SET and CIP2A
expression were elevated in breast cancer cell lines. Importantly, this observation was validated with alternative
methods in both cell lines and in breast cancer patient tumor samples. The observation of increased SET and
CIP2A mRNA levels correlates well with the well-established observation of
enhanced c-MYC expression and activity in breast cancer. This was further validated with one
final experiment: assessing if SET and CIP2A modulate c-MYC transcriptional
activity. Using RNAseq again, the
authors looked at genes known to be under the transcriptional control of
c-MYC. As expected deregulation of
SET and/or CIP2A decreases phosphorylation of c-MYC and leads to an overall
decrease in MYC’s activity.
Where do we go from
here?
As for patients, NGS, has the capability to provide large
amounts of data to the cancer researcher regarding alterations in their cell
lines, tumor models, or drug treatments.
Currently, NGS provides the most accurate, in-depth read-out for genetic
information.
Although the use of this technology, particularly RNAseq, in
the basic research laboratory is growing in popularity, several concerns
arise. First of all, like in clinical
settings, NGS produces very large datasets that need to be sifted through,
organized, and made understandable.
For a basic laboratory without the appropriate personnel and training,
this is a major roadblock.
Additionally, outsourcing NGS technology can be costly and often times
this cost is prohibitive to basic scientists on tight financial budgets. For those that overcome these hurdles,
such as the authors of our current article, adding NGS technology to your study
is attractive and can give your paper the push it needs to get published.
Scientists possess additional tools, each with their own
advantages and disadvantages, to answer similar questions. And we must decide which tool is most
appropriate. I would argue that in
the current study, using RNAseq, screening the entire transcriptome, to assess
levels of a select handful of genes (out of 10s of thousands) seems
excessive. Less costly, more
focused approaches would yield the same answer.
Other concerns arose while reading this paper that center on
the use of appropriate controls and validation of their results with
alternative methods. However, the
story is convincing: inhibition of SET or CIP2A inhibits c-MYC and tumor
progression in a breast cancer setting.
This piece of information provides a direction for the development of therapeutic
interventions focused on c-MYC.
It’s not clear that NGS was necessary to reach this direction, but it
helped move the story along way.
Today’s CancerUncovered morsel: watch for more NGS in the
research laboratory as it gains momentum!
References:
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