More Studies Needed to Improve Detection and Classification of Fallopian Tube Tumors

Giampero (John) A. Martignetti, MD, PhD

The road of research in gynecologic malignancies needs to take a turn toward improved detection methods and classification, experts say, in order to both understand the biology of a patient’s tumor, as well as the biology of mutations they may harbor that are not driving the cancer.

Giampero (John) A. Martignetti, MD, PhD, explains that more large-size clinical trials need to be conducted to answer some of the burgeoning questions in the fallopian tube and ovarian cancer landscape.

“We have seen new biomarkers come and go; they have all been in the literature, but they don’t make it out to the bedside,” said Martignetti. “We have got to change how we collaborate. We need to bring more patients together. We need to treat them and think about them under an umbrella, and not just have all these patients floating about. We need to bring patients together so we can do these kinds of large-scale studies.”

OncLive: What did you share in your overview on fallopian tube cancer?

How has this type of technology evolved in recent years?

In an interview during the 2017 OncLive^® State of the Science Summit^TM on Treatment Options in Ovarian Cancer, Martignetti, a professor of genetics and genomic sciences, oncological sciences, obstetrics, gynecology and reproductive science, and pediatrics at Mount Sinai School of Medicine, discussed evolving methods to detect gynecologic malignancies, with a focus on fallopian tube cancer.Martignetti: The talk really focused on detection methods in gynecologic ovarian cancers, specifically, and trying to stress the history of how we got to where we are in detecting cancers, how to think about what it means to detect them, and some of the technology that goes on behind the scenes in detecting these cancers. It is primarily focused on circulating tumor (ct) DNA. The one thing that I also wanted to get across in the talk was the concept that this is a brave new world, in the sense that next-generation sequencing technologies have really changed what we can see. In the past, we have relied on x-rays, clinical exam x-rays, CT scans, but now the level of detail that we are getting is unheralded. That can be both a good thing and be pretty scary, too.

The reason it’s a good thing—and we have shown this through our own work at our institution and others have shown this not only in gynecologic cancers, but a number of different cancers—is that ctDNA can not only detect cancers earlier than current standards, but it can provide prognostic information, what the patient will probably respond to or not respond to in the future, and can provide information on targeted therapies. Even now, in some recent literature, data suggest potential for use in screening.

It’s a brave new world because those are all fantastic things. However, in one recent study that we have looked at it—and now we’re finding it in other studies that others have done in different cancers—it turns out that we have assumed the genetics of cancer. If you have particular mutations, we have always assumed that these are cancer drivers and you will get the cancer or you have that cancer.

It turns out, there may be a lot of people walking around with these mutations. They are bonafide mutations; these are not artifacts. They are walking around with them and could be in the uterus, skin, bloodstream—and you don’t have cancer.

How would you like these detection methods to go a step further?

Are there any ongoing trials designed specifically for fallopian tube cancer?

That really changes how we must think about things. It’s not enough to say that we have detected mutations. We need to understand what that means in the context of cancer evolution with these new technologies. That is really the brave new world part of this. We are at the point now of having not only cancer-driven genes, variance of unknown significance, and actionable target genes, but we are really at a point where we have mutations of indeterminate potential. The technologies are here now where we can do these things. They are developing and being refined. What we need to do is have more clinical studies timed together with the technologies, and with the outcomes and with the clinical interpretations. This means marrying, essentially, the people who do the technology with the people who take care of the patients. It is not just one-sided; it must be a communication between those 2 areas of expertise to try moving the field forward. Historically, cancers have been defined by where they arise. But, indeed, you do need to break these cancers down by their histology, as it turns out. Understanding the histology of a cancer is particularly important on the kinds of treatment and outcomes you would expect.

What are the major challenges that continue to exist with fallopian tube cancer?

Then, you have to go the other way, too. It turns out that as you start looking across cancers and because the number of mutations are shared [in multiple malignancies], some of the targeted agents [that work in one type of cancer] might work in ovarian cancer. It really goes both ways; you have to break it down histologically, but you also need to bring it together or separate it genomically to get to the next level of treatment. Indeed, patients are slowly doing better. The reality is that the 5-year overall survival for ovarian cancer in particular has not changed dramatically. It is changing slowly and it has been pointed out by others that surgical techniques are improving, a number of other agents are here and being used, but it’s still lagging. We really need to make better inroads to treatment.

That being said, [there is a] value of the molecular approach, either with ctDNA or other kinds of technologies, and thinking about the tumor, the degree of tumor heterogeneity and the number of mutations. It is not just when you look at a tumor in bulk, but when you can disassociate it, look at each individual cell, and think about the mutations that exist within each of these.

On a single-cell level, we need to try understanding what that tumor is truly made of. We need to understand how we can detect it earlier and classify these tumors. What are the genomics of that mutation within that whole tumor? How does that tumor distinguish itself from patient number 2 or patient number 3? Then, we can start putting that data together. We probably have a lot more work to break things down before we can start pulling things together. That is where we need to go.