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A fast-tracked paper on a possible new ovarian cancer screening tool by Lance Liotta , M.D., Ph.D., of the National Cancer Institute (NCI), and Emanuel Petricoin, M.D., of the Food and Drug Administration (FDA), appeared electronically in The Lancet on February 7, 2002. BenchMarks interviewed the scientists about this new tool, which employs a technique called proteomics. The researchers talk about the tool, how it was developed, and its potential applicability to screening and treatment for ovarian and other cancers.

Dr. Liotta and Dr. Petricoin run the FDA/NCI Clinical Proteomics Program.

Are there cancers other than ovarian cancer that would be obvious targets for this new technology? If so, why would those cancers be better to study than others?

Dr. Liotta: As we speak we are conducting additional research on other cancers, and we're planning to do more in the future. An example would be prostate cancer. We are looking for cancers that have an indolent phase -- an asymptomatic, early silent stage of progression. If we can pick up the cancer at this early stage we may be able to treat the tumor before it has metastasized. We chose ovarian cancer and prostate cancer because of the urgent clinical need. We were very fortunate to be able to collaborate with David Fishman at Northwestern University, who has a high-risk clinic and a very good population of samples with five-year follow-up. This is a valuable resource.

Are there other cancers you will eventually focus on?

Dr. Petricoin: Breast, pancreatic, colon and lung -- some of the most common solid human tumors. We're now focusing our attention to validating ovarian cancer in larger study sets. However, we're not just focusing on early cancer detection. We have a clinical trial that Dr. Elise Kohn of NCI is running (see accompanying story) where we're going to assess the ability of this new method to detect relapse in women with ovarian cancer.

We are also using this technique to look at early drug toxicity. In the future, if we can predict by protein patterns which patients are likely to have an early toxic response, we could treat them at lower doses or switch to a new therapy choice.

What are the advantages of looking at protein patterns rather than looking at a single biomarker to detect disease?

Dr. Petricoin: In the past, scientists have taken a reductionist view of biomarker and drug target discovery and the subsequent use of these biomarkers, one at a time, to detect disease. The new concept we are proposing is that patterns of serum proteins can be used as diagnostic endpoints -- the pattern itself is the diagnostic.

Instead of looking at one biomarker at a time, you can take an information archive, such as the serum proteome, and look at tens of thousands of proteins and peptides at once -- a proteomic portrait, a barcode, if you will -- that we generate very rapidly from the mass spectrometer.

By eye you would not see any underlying hidden patterns -- it would look like a chaotic picture. The human brain doesn't do well with that kind of pattern recognition. We've taken a machine-based approach -- an artificial intelligence-based algorithm to look at hundreds of millions of combinations of patterns of proteins very quickly and find the most optimal pattern that segregates cancerous samples from noncancerous samples in the training sets.

In this case, the sets are sera from normal healthy women and from women with active ovarian cancer at the time the serum is taken. The algorithm is trained on those samples first. The outcome is already known in the samples in the training sets.

This algorithm was trained on this complex pattern of tens of thousands of proteins and peptides in the serum and it found a combination of five proteins that segregated those sets 100 percent of the time.

Then we challenged that algorithm with blinded, unknown sera specimens, and it was able to take that combination of five proteins, and say, 'Does this pattern look like the pattern from normal subjects or does the pattern look like patients with cancer?' In this blinded set, we found these great results! And now, we're going to attempt to validate these findings in much larger sets and find out how well we really can do.

Dr. Liotta: Let's say you look at a population of patients with cancer. In 20 percent, maybe, one marker works, but it's not good in the other 80 percent. In another 20 percent a different marker is best.

The best way to predict cancer in an overall population might be a combination of markers that transcends that heterogeneity. The marker is a pattern formed by a group of proteins, rather than one protein. Perhaps one marker is an old idea. The new idea is to look for patterns or groups of markers, rather than one marker at a time. We have shown support for that hypothesis in ovarian cancer.

This paper focuses on protein patterns, but could this technique help elucidate pathways or signals between proteins?

Dr. Liotta: Another application of this technology is to look at patterns of protein changes, such as phosphorylation or cleavage, or patterns of interactions of proteins. If we sort through large number of interactions and then correlate the numbers with the state of a signal or look at changes before and after drug treatments, then we can use this method to build cell circuit diagrams, because we can figure out who is talking to whom and who is connected to whom.

Dr. Petricoin: We're developing platforms that can look at early-stage serum-based portraits and we're inventing platforms that can look at hundreds of different phosphorylation endpoints simultaneously. And given that constellation of changes in patterns of protein phosphorylation, we can begin visualizing the critical 'gates' in the cellular circuitry or in the wiring diagram of the cell.

We can look at the changing patterns of phosphorylation, relate that back to what we see in the phosphorylation patterns when a patient is treated with a therapeutic, such as Herceptin. We can look before and after the treatment and determine whether that patient responded to treatment and correlate that back with the signaling patterns.

Patient-tailored therapy can then be achieved as each patient is profiled beforehand for the state of their cellular circuitry in say, a biopsy, and the correct choice of therapy is matched to the pattern seen.

The success of the test in correctly diagnosing cancer is remarkable in this small sample. How important is the five percent false positive rate?

Dr. Petricoin: I think it's especially important to be cautious about the utility of this system for general population screening. Our false positive rate is only five or six percent right now. But when a large population of women is screened, this will produce many women who test positive but are actually without ovarian cancer who will be worked-up unnecessarily.

The first application would be for women in high-risk clinics. It's very important in ovarian cancer, because it's a rare type of cancer, to have almost 100 percent specificity (identifying every woman in the group who has cancer), even if that means sacrificing some sensitivity (not identifying correctly every woman who doesn't have cancer) .

This is why you see this first application being applied to high-risk clinics. Even with a false-positive rate of five or six percent, you can have a positive impact among women who have, say, a mother or sister with ovarian cancer or are known to have a BRCA1 mutation.

These women are already thinking about prophylactic oopharectomies. They're enrolling or referred to these high-risk clinics because their anxiety level is very high. If we were able to give the physician something else to help him go in and find out for sure that patient has something wrong or could assure the patient that they are okay, that would be a dramatic help in and of itself. Also, in that setting you could accept a higher false-positive rate because you would be looking at an enriched population with the potential to harbor the occult (asymptomatic) disease.

Dr. Liotta: A diagnostic test that is negative can provide reassurance to a patient who is worried about cancer risk.

This technique is minimally invasive, requiring a finger prick's worth of blood. Do you see screening for cancer evolving to methodologies such as these and away from radiology and more complex and costly techniques?

Dr. Liotta: We view this as supplementary and complementary to other tests. The most accurate means of diagnosis will be a combination of all of these methods. You can screen patients with this kind of method and then more efficiently choose who should get a thorough radiologic work-up or physical examination. This kind of methodology combined with other marker tests or genetic screening might end up being a good combination, much like a combination of different protein testing methods might be better than one marker.

Dr. Petricoin: We don't see this as being a stand-alone endpoint. Rather, we see it as something that hopefully will give physicians something to refer to in addition to all the other tests they have at their disposal. The physician is the ultimate intelligence tool to decide what's best for the patient.

Is there evidence indicating a possible function of any of the key proteins in the pattern used to diagnose ovarian cancer?

Dr. Liotta: We don't know what the proteins are. We're investigating their identity. Nevertheless, even though we don't know what they are, the pattern is still discriminatory. When we find out what these proteins are, they may or may not provide a clear understanding of why they're altered. They could be several orders removed from the pathological source.

Dr. Petricoin: PSA is a very widely used biomarker and the fact that it is a cystein protease does not add additional value to its utility as a biomarker for prostate cancer or prostate volume. So knowing what a biomarker is and how it relates to the biologic underpinnings of the disease is a separate issue altogether from how well it behaves as a specific and sensitive marker for the presence of a disease.

What trials are currently under way to further evaluate this technique for the detection of ovarian cancer? When are these trials expected to be completed?

Dr. Liotta: We have an ongoing trial looking at patients who are being treated for ovarian cancer to see if this technology can pick up early recurrence. That trial is open to accrual and the P.I. (principal investigator) is Dr. Elise Kohn, NCI. In addition, we are developing a whole series of future confirmatory trials in multiple institutions and with cooperative groups to test this as an early diagnostic tool over a larger population. It should take about six months to initiate all these trials.

Dr. Petricoin: For a researcher who has an interesting bioinformatics platform but doesn't have a clinician and can't get access to patients or doesn't have a mass spectrometer, they can download the raw spectra from our study set and evaluate their own tool with their own program. At the same time, we'll be expanding our mass spectra proteomic profiles with our ever-expanding studies and, like the government did with the human genome sequencing effort, making it all open access as we generate patient spectra whose data are posted anonymously, under IRB (institutional review board) rules, on the Web. Hopefully we'll have thousands and thousands of these spectra on the Web that people can easily access, and we think it will be a great resource for the scientific community.

Of the thousands of proteins in the blood, what criteria did the artificial intelligence program use to identify those that would be most useful in discriminating between cancer and non-cancer? How many proteins or peptides were needed to create a reliable pattern?

Dr. Liotta: There was no preconceived information. All it did was look at the patterns and correlate with the training set and correlate with disease or not disease. It was a survival of the fittest pattern.

Dr. Petricoin: Here are 50 women who don't have cancer, and here are 50 women who have cancer and here are the spectra of each of these women. Each of the spectra is comprised of 15,200 data points -- one for every protein or peptide. At a precise molecular weight, a defined intensity value reflective of the relative abundance of that protein is given. We can look at all the intensities of each oneof those precise molecular weight points as individual data points and find the optimal pattern to segregate the two training sets 100 percent of the time using the combinations of intensities in N-dimensional space.

Once we found that pattern we then tested it on unknown samples. We also made sure that we tested the pattern on women with common benign gynecologic conditions such as ovarian cysts, fibroids, endometriosis, and general inflammatory diseases. It is much more common to have women with these benign conditions. Markers like CA-125 can be elevated in women with these benign conditions.

Ovarian cancer is so rare, that for every one woman with ovarian cancer, many, many more are going to have ovarian cysts, have uterine fibroids. So biomarkers that detect these conditions indiscriminant of ovarian cancer are not really useful. We trained the artificial intelligence program to see sera patterns from women with ovarian cysts by including these samples in with the healthy cohort, therefore the patterns that discriminate ovarian cancer from healthy also discriminate it from benign conditions.

The exciting thing about artificial intelligence programs is that they can learn and get better over time as more data are brought in. As we validate this in larger trials, we envision an ever-growing and expanding training set of spectral data. We can then build models from this for whatever we want -- we might later want to look at different groups of women with ovarian cancer. The models get better and better as they see more data.

Where does this advancement in the field of proteomics leave the study of genomics? Will it no longer be as important to know what genes are defective or unexpressed but rather know what proteins are non-functional or which pathways have been interrupted?

Dr. Liotta: Information we get from proteomics we cannot get from genomics and vice versa. Genetic mutations and alterations in DNA and chromosomes may not be known at the protein level and the same is true the other way. Whether a gene is transcribed to a high or low level may have nothing to do with what happens to the protein product - whether it is phosphorylated or whether the protein product of the gene binds to another protein partner. Genomics and proteomics are in some ways exclusive and also complementary.

Dr. Petricoin: Putting myself in the shoes of a patient, I would want to have the full benefit of what science has to offer. Dr. Liotta and I believe that genomic and proteomic approaches should be, and will be, used as complementary technologies to benefit and positively affect patient outcome -- that's really what our proteomics program is all about.

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