Name: Federico Monzon
Title: Director of molecular pathology, Cancer Genetics Laboratory, Baylor College of Medicine
Background: Director of molecular pathology, Cancer Genetics Laboratory, Baylor College of Medicine, 2011-present; associate professor of pathology, Weill Cornell Medical College, 2002-present; founder, head of scientific advisory board, iKaryos Diagnostics, 2007-2011
Education: MD, Universidad Nacional Autonoma de Mexico, 1992; postdoc, University of Pennsylvania School of Medicine, 1992-1993; residency, Thomas Jefferson University Hospital, 2002; fellowship, Children's Hospital of Pittsburgh, 2003
Federico Monzon's talk at the joint Human Genome Meeting and International Congress of Genetics, held last month in Singapore, was supposed to focus solely on using microarray-based gene expression analysis to identify tissue of origin from tumor samples.
Instead, Monzon, who is director of molecular pathology at Baylor College of Medicine's Cancer Genetics Laboratory, branched off to discuss, among other topics, his use of high-resolution SNP genotyping arrays including validation of Affymetrix's CytoScan platform; as well as using exome sequencing together with chromosomal microarrays to identify pathogenic mutations in solid tumors from pediatric patients.
Before joining Baylor in 2011, Monzon helped found a molecular testing company called iKaryos Diagnostics, which relied on array technology (BAN 9/13/2011). Though funding issues ultimately forced iKaryos to end its operations, Monzon has continued to apply genomic knowledge as a clinical tool for diagnosis, prognosis, and therapy selection in cancer in his new role at Baylor.
BioArray News spoke with Monzon following his presentation at HGM/ICG. Below is an edited transcript of that conversation.
How long have you been using microarray technology in cancer diagnostics.
And you were originally using the 250K?
Actually, we started with the 10K array back in the day, 2003, but then we switched to the 250K. The 250K was a very robust platform on paraffin-embedded tissues. It gave us great performance and it had very good coverage for the clinically relevant regions, especially for hematological malignancies. If you were looking for large genomic changes, that platform provided adequate clinical sensitivity. We had a publication with Jill Hagenkord [who worked with Monzon at the University of Pittsburgh and at iKaryos and has since joined Bay Area startup Invitae] where she compared different platform resolutions, and the 250K was the one we decided to use clinically at the time. So, for paraffin-embedded tissues, that was one platform that was robust, but we are currently validating CytoScan and it is performing very well.
Why did you decide to move to CytoScan? I know that CytoScan was originally developed for constitutional work.
There are several reasons we want to move to CytoScan. Number one, better coverage, so we can see more of the genome. Number two, we can have a single workflow in the laboratory. Having two parallel workflows doesn't serve us very well. If we can consolidate on a single SNP array, that would make our laboratory work better.
Affymetrix filed CytoScan with the US Food and Drug Administration earlier this year to have it cleared for constitutional postnatal testing. If it is cleared, will that have any impact on its use in cancer cytogenetics?
All laboratories currently using arrays are using them as laboratory-developed tests and there are advantages and disadvantages to offering LDTs versus an FDA-cleared product. The advantage of having an FDA-cleared product is the standardization across laboratories. The advantage of having LDTs is the ability to change platforms and improve in a more linear manner. I think there are both pros and cons. Will it change anything for us? Probably not. Most of our constitutional arrays and even some cancer arrays, such as our solid tumor array, are manufactured by Agilent Technologies. We are probably not going to change that just because there will be an FDA-cleared array on the market. Whether in the future, it will make a difference, I cannot say, but at this point, it won't be a big change for us. I still think it's a positive development in the microarray field.
You mentioned in your talk that you were using arrays in neuropathology, making a diagnosis on genetics rather than morphology.
Well, you don't only make [a diagnosis] based on either. There is the clinical picture, there is the morphology. You have to take the morphology into account. So, it's not that we are only going to use arrays. It's just one of the pieces of the puzzle, and it is an additional piece that has key information that allows you to say, 'Okay, this really is something different than what I had thought,' once you put all those pieces together. And you would come to the same conclusion if you used fluorescence in situ hybridization or conventional karyotyping. You would find the same abnormalities and reach the same conclusion. It's just that with this platform, it enables us to do it on samples that might not have enough [cells] grown for conventional karyotyping. It gives additional information and more tools for laboratories to get to find the genetic events that can help diagnosis or prognostication.
CytoScan isn't the only array Affymetrix has targeted for cancer research. They also have a tool called OncoScan. Have you used it?
OncoScan right now is available as a service. It is a very robust platform. It performs very well. We have specimens that we have tried with either the CytoScan or the 250K, and they have failed because they were extremely degraded paraffin-embedded tissues, and we have actually used the OncoScan product to get information out of those. I would say in 99 percent of the cases, we got very good data out of the OncoScan platform. So I am really looking forward to seeing that platform become available in a kit format that anybody can use.
Would you use OncoScan instead of CytoScan?
I would, because the performance on paraffin-embedded tissue is much better. The data is much more robust. When you do a paraffin-embedded sample on CytoScan, the data is much noisier compared to a fresh sample. Though the data that you are getting is noisier, you have good confidence that the abnormalities that you are seeing are real, but your resolution is not as high, so you cannot … detect 350-kilobase deletions in paraffin-embedded tissue using CytoScan. It would probably be bigger regions, because of the noise. But the OncoScan has a much better performance. The data is much cleaner for paraffin-embedded samples. I think that if the OncoScan would be available, I would evaluate it to see if we could move our paraffin-embedded tissues to that platform.
You mentioned you have used arrays in renal cancer, hematological cancers, brain cancer. Are those your areas of focus or is the technology suited for those cancers?
I think those are the cancers where chromosomal analysis has already been established. So those are the low-hanging fruit. Most of the people dealing with these types of tumors are already used to asking for genetic information, either by requesting [fluorescence in situ hybridization] analysis or karyotype analysis, so transitioning that kind of testing onto an array is easy. It's natural to want to see more, to get more information. Those are also the areas where the clinical evidence is stronger for you to be able to use the information that you are gleaning from the arrays and translate it into a clinical decision.
You can do these on almost any other tumor. The question is what are you going to do with the information? We don't necessarily know for many types of tumors what a deletion here or there means. There is a lot of research going on in these areas. For some tumors, there is a lot that is known, for others less.
For example, in our laboratory, we are running advanced breast cancer on a fairly routine basis. Our oncologists want to know what else is going on in these tumors, especially those that are resistant to other therapies, and sometimes they order these. There is extensive literature on chromosomal abnormalities, and some of them have been shown to have some prognostic value. So we are running those also. There are different tumors and different applications you can do. I guess the ones I described in my talk are the ones where there is more of a consensus about the use of genetic changes in the tumor towards clinical decision making.
You also discussed the use of exome sequencing and using arrays to assist in the analysis.
Yes, we have a project headed by Sharon Plon and Will Parsons from Texas Children's Hospital. Their project is to study the clinical utility of cancer exome sequencing in pediatric solid tumors. The gist of it is that we perform exome sequencing on these solid tumors and then we are studying how to deliver the information to the oncologist and to the patient, and how both the oncologist and patient are using that information.
One of the things that we have found in the first 20 or so cases that we have done is that we have got a large number of mutations. In some cases, we see mutations that are clearly described in the literature and we understand well what their role is in the pathogenesis of the tumor, either in prognostic or therapeutic value of those mutations.
But then there are large numbers of mutations in new genes that have not been described related to these tumors. So how do we go about interpreting that data? That is the challenge. On the research side, we are starting to look at using chromosomal microarrays to help guide us to what genes are relevant to the disease. We can see which of these mutations is associated with a copy number abnormality. Those we can rank higher, because they are more likely to be drivers of pathogenesis. At least, that is the theory.
The idea is that we can get a lot of information out of the exome, but copy number analysis allows us to better interpret that data, to get a more integrated view of what is going on in that tumor. So that is how we are going to use copy number analysis to complement exome sequencing. We have done a few cases and it works very well, so we are in the process of implementing that.
I can imagine the cost of a case can grow exponentially once you move from exome sequencing to chromosomal microarray analysis.
This is part of an [National Institutes of Health]-funded study, so these exome sequencing analyses are being funded through the study [Plon and Parsons received a $6 million grant from the National Human Genome Research Institute and the National Cancer Institute to support the project in 2011 — Ed.]. Chromosomal microarray analysis — we are currently looking at how we are going to fund that. This is not going to be paid by the patient. We are looking at internal ways at doing that.
What is your take on the Cancer Cytogenetics Microarray Consortium? Do you think it will achieve its goals?
We are in the process of finalizing the manuscript [on a platform-comparison study]. The platforms are very mature; they are ready for use in the clinical field; they are reproducible across laboratories and across different tumor types and across different sample types; and I think that was the stated goal of that study. So the CCMC achieved its goal of evaluating whether these platforms were ready for clinical use with cancer, and I think that goal has been achieved, and we'll have the manuscript soon.
CCMC membership continues to grow, but there are still some who are hesitant to adopt the technology.
I think there are several factors that are impacting that. One of the big factors is the reimbursement environment. Many laboratories are uncertain what is going to be the reimbursement for arrays. I think people are cautious of investing in relatively expensive tests and not getting adequately reimbursed. I think that has dampened the enthusiasm in many laboratories to move forward.
The other part is that there is a lag in clinical adoption. Microarrays in constitutional diseases have been adopted very strongly and people use the technology all the time. For cancer, it's a different bar that you need to set, and people want to see more evidence on how it will impact treatment decisions, and at the same time we are still getting a lot of information for the large majority of tumors. There is a fine set of tumors where we know what the information means, but there is a larger number of tumors where it is still not clear how the information is going to impact clinical decision making. So once the reimbursement coalesces and there is additional information out in the literature about these cancers, then I think we will see greater adoption.