Ali Amin-Mansour: Good morning, everyone. My name is Ali and
I'm a computational biologist from Dr. Levi Garraway's lab. Today I will speak about somatic
alterations in clinically-relevant cancer genes among 12 TCGA cancer types. This presentation
is based on a project that was done in collaboration with Dr. Han Liang of MD Anderson and is currently
in press.
Okay, so I don't need to tell the people in this room how much TGCA has helped identify
alterations that may contribute to many different tumor types. However, less attention was given
to this data with a clinical lens. As these profound technologies that enable TGCA move
into the clinic, an understanding of the landscape of clinically-actionable somatic alterations
may inform patient care.
So now the question is: what does the spectrum of alterations in clinically-relevant genes
look like across tumor types? Somatic mutations and indels were called from 3,276 TGCA patients
and we focused on clinically-relevant cancer genes. So what are these genes? From our perspective,
these are genes that, when somatically altered, can predict response or resistance to known
therapies where it may have diagnostic or prognostic utility. So we created the target
database, which is a set of genes that we defined using expert opinion and literature
to be clinically relevant in cancer. This database is online as of about 10 days ago
and here's a link to it.
At the time of the analysis, this list contained 121 genes. We know that this is already out
of date, since it's very difficult for a single group sitting in one office to keep track
of them, and we think we could do a much better job as a community. So we want to encourage
you to contribute to this list if you have any genes that you would like to nominate.
Now, among these 12 tumor types, we identified over 600,000 mutations or short-insertion
deletions and, of these, a little less than 500,000 were non-synonymous. Of these non-synonymous
mutations, over 10,000 alterations occurred in clinically-relevant cancer genes in about
90 percent of the patients. Now, we do recognize that not all alterations are drivers or are
known to be clinically-relevant, but we do want to highlight them to promote further
study of these alterations and to better place them in a clinical context.
Here you can see the frequency of alterations across the 12 tumor types in the clinically-relevant
genes. I know it's impossible for you to read the names of the genes, but what I'm trying
to demonstrate here, from a 30,000-foot view, is this long tail of alterations in clinically-relevant
genes. Now, you might be wondering what the highest peak here is. It is TP53. Before you
roll your eyes, I want to say that we do recognize that it remains unclear what the clinical
relevance of TP53 is from a therapeutic standpoint, but it remains an entry criteria for many
clinical trials; therefore, we feel that it's worth including here. There has also been
some very interesting clinical trials emerging with Neratinib. Recent evidence shows that
patients with ERB2 mutations show response to Neratinib.
I've also included a couple of other mutations, but rather than going through every single
gene here, I would like to highlight a few key themes that emerged from this analysis.
So, Theme One: Right now many clinical centers are using hotspot-profiling technologies for
clinical use. These include technologies, like MASPEC, that find individual, predefined
events in a given gene. Here we have taken a representative hotspot profiling approach
called Oncomap that looks at over 400 alterations in 41 genes. And we ask by tumor type, with
this technology, how many patients have at least one alteration in this panel? As you
can see, this does cover a reasonable territory.
Now, if we perform whole-exome sequencing and take the same set of genes and ask how
many of our patients have alterations within the same list of 41 genes; as you'd expect,
we do find a lot more patients with alterations in this gene set. Expanding next-gen sequencing
into the clinic will help us identify more clinical alterations than hotspot profiling.
This can help us to take more clinical: genotype to phenotype relationships. Now, one might
argue, this objective might be achieved if you just looked at targeted panels, but you
have to keep in mind that the list of clinically-actionable genes continues to increase and the cost of
doing whole-exome sequencing is approaching that of doing targeted panels without the
need to re-sequence as information about new genes emerge.
Theme two: hotspot and other alterations in well-known clinically-relevant genes occur
rarely in unexpected tumor types. For example, as expected, we observed BRCA2 alterations
in ovarian and breast cancer. However, when excluding patients with mutation rates of
higher than 10 per megabase, we observed BRCA2 alterations in multiple tumor types. These
alterations may have clinical relevance and are generally not thought of in these tumor
types, as these mutations may predict sensitivity to PARP Inhibitors or Sisflaten-based [spelled
phonetically] chemotherapy, so they may have clinical relevance. For research purposes,
this may not be a significant gene in this tumor type, but for the few patients with
this alteration in a cohort of, let's say, 500 patients, this is clinically significant
and needs to be identified.
Theme three: genes that are rarely mutated in one tumor type occur frequently when considered
across several tumor types. As an example, TSC1 alterations may predict sensitivity to
MTOR Inhibitors, at least in bladder cancer, as was demonstrated by David Solit at Memorial
Sloan-Kettering. However, having clinical trial and only one tumor type is not feasible,
but if you look at a TSC1 mutation status across tumor types, you can imagine a clinical
trial. These types of trials, the so-called basket trials, are emerging and this is just
one of the many examples where genes rarely mutated within one tumor type become frequently
mutated across tumor types.
In addition, we also recently learned that certain MTOR mutations may predict sensitivity
to MTOR inhibitors, like everolimus. As with TSC1, MTOR mutations occur rarely in any given
tumor type, but in aggregate, they provide a rationale for a similar basket trial. Thus,
if you consider low-frequency alterations across tumor types and within pathways, one
can further imagine clinical trials that can be designed with this knowledge.
So, our ability to increase the link between alterations and cancer genes and clinical
trials is improving. Here, we downloaded all of the clinical cancer trials from clinicaltrials.gov
from 2005 to 2012 and asked whether they had the name of a clinically-relevant gene in
their title. You can see that the number of trials that incorporate clinically-relevant
genes continues to increase. Now that we've identified the landscape of alteration among
the list of clinically-relevant cancer genes, we have the opportunity to design the next
wave of rational clinical trials.
So in summary, what we saw today was a long tail of alterations in clinically-relevant
genes. We also saw that going from hotspot profiling to exome sequencing will yield a
more complete and clinically useful patient-tumor profile. We also showed that clinically-relevant
alterations in well-known genes occur rarely in unexpected tumor types, and also, genes
rarely mutated in any given tumor type are more regularly altered when considering aggregate
studies. Broadly, we know this is just a start. To improve on these analyses we are moving
to integrate additional data types that may have clinical impact, such as copy number
variations or targetable fusions, such as ROS1 fusions in lung cancer. We hope that
this work will kick start more OMeG studies with a detailed clinical focus.
I would like to thank Dr. Garraway for his mentorship and guidance, Dr. Van Allen and
Dr. Wagle, Gad Getz and Mike Lawrence from the CGA group, Dr. Han Liang and Yuan Yuan
from MD Anderson and Larsson Omberg. Also, I would like to thank the TCGA organizers
for giving me this opportunity to present.
[applause]
Male Speaker: Well, we have a few minutes if there're any
questions.
Male Speaker: Hi. In theme two, where you showed mutations
in BRCA gene appearing in other unexpected cancer types, were these mutations that were
recurrent across cancer types or were the specific mutations specific to those unexpected
cancer types? So, I'm talking about position-wise. Are these hotspots or are they --
Ali Amin-Mansour: No, they weren't. They occurred in different
positions.
Male Speaker: In different positions?
Ali Amin-Mansour: [affirmative]
Male Speaker: Okay. Do you think that's important whether
they occur at the same position or not, you're looking across cancer types?
Ali Amin-Mansour: Well, it might be more interesting. Well,
I -- what we want to say is that it's worth taking a look at. I don't know whether they're
useful information, whether they're driver events, but it's worth taking a look at.
Male Speaker: Thank you.
Ali Amin-Mansour: Okay? All right. Thank you.
Male Speaker: Thank you, Ali. The next presentation is by
Layla Oesper on Inferring Intra-Tumor Heterogeneity from Whole-Genome/Exome Sequencing Data.
[end of transcript]