3 Questions on…
Answers straight from the experts on the latest news and topics in oncology
Friday, April 15, 2016
With LAURIE MARGOLIES, MD, FACR, Director of Breast Imaging at Dubin Breast Center of Mount Sinai Hospital
By Sarah DiGiulio
Screening mammography has been a topic of debate in breast cancer diagnostics due to a shifting consensus (or lack thereof) over the age women benefit most from having the test (OT 3/10/14 issue). But new findings from a group of breast imaging experts, cardiologists, and internists, bring a whole new variable to the equation.
The researchers found that breast arterial calcification, as assessed by digital mammography, is a strong predictor of coronary artery calcification—and is a superior predictor of women at high risk of cardiovascular disease compared with other standard risk factors, according to their study presented at the American College of Cardiology Annual Scientific Session last month and recently published in the Journal of the American College of Cardiology Cardiovascular Imaging (2016;9:350-360).
"Mammograms can serve a dual function," the study's lead author Laurie Margolies, MD, FACR, Associate Professor of Radiology at Icahn School of Medicine and Director of Breast Imaging at Dubin Breast Center at Mount Sinai Hospital, explained in an email.
"The same mammogram exam can screen for breast cancer and look for evidence of cardiovascular disease. This dual function can be accomplished without additional radiation to the patient; the patient's mammogram would not take any longer; and it would add only seconds to the interpretation and reporting time for the breast radiologist," she said.
For the study, Margolies and her colleagues screened 292 women using both digital mammography and nongated computed tomography, and then compared how closely the breast artery calcification scores were related to the coronary artery calcification score. The data showed a sensitivity of 63 percent, a specificity of 76 percent, a positive predictive value of 70 percent, a negative predictive value of 69 percent, and an accuracy of 70 percent.
While additional confirmatory data would be necessary to make recommendations on cardiac risk assessment, the authors note in the study, "The opportunity to significantly improve the identification of high-risk women by further simple analysis of a broadly used screening tool should be intensively evaluated in larger patient cohorts."
Here's what Margolies said cancer care providers should know about the research.
1. Could you elaborate on why these findings suggest mammograms might also be useful in identifying women at risk for cardiovascular disease?
"A key finding in this study is that breast arterial calcification is a very strong independent risk factor for the presence of coronary artery calcification—more significant than age or hypertension. And there is actually a strong quantitative correlation between breast and coronary artery calcification.
"When the information about breast arteries is added to traditional risk evaluation, its significance is even greater. The positive predictive value of about 70 percent is as good as Framingham Risk Scores or the 2013 Cholesterol Guidelines Pooled Cohort Equations for identification of women at high risk of cardiovascular disease."
2. Is this the first evidence that mammography might play a role in predicting heart health? Is the evidence sufficient to suggest mammograms can or should be used for more than just diagnosing breast cancer?
"Breast arterial calcification has been the subject of previous research, and most studies have shown a correlation of breast arterial calcification with myocardial infarction, stroke, and other cardiovascular diseases. As Drs. Nasir and McEvoy note in their editorial accompanying this research (J Am Coll Cardiol Img 2016;9:361-363), the reporting of breast arterial calcification has not though been incorporated into reporting guidelines and health care delivery protocols.
3. What should practicing oncologists know about the findings from this research?
"Cancer care providers should know that there is a strong correlation between breast arterial calcification and cardiovascular disease. This suggests that women who have breast arterial calcification might benefit from further testing such as gated formal cardiac calcification scoring and/or preventative treatments such as diet modification, exercise, smoking cessation, medication, or other strategies. Practicing oncologists can and should incorporate breast arterial calcification data into their patients' overall treatment plans.
"Primary care physicians and others receiving mammography reports do, however, need education about the linkage of breast and systemic arterial calcification. And this study will hopefully add momentum to the increasing awareness and discussion of women's cardiac health."
Friday, March 18, 2016
With RICHARD L. SCHILSKY, MD, FACP, Chief Medical Officer of ASCO
As of Monday, the American Society of Clinical Oncology's first clinical trial opened for enrollment of patients. The trial—the Targeted Agent and Profiling Utilization Registry (TAPUR) study first announced at the 2015 ASCO Annual Meeting (OT 8/25/15 issue
)—was designed as a multi-arm, Phase II, basket study to evaluate molecularly-targeted cancer drugs and collect data on clinical outcomes of patients on these drugs used outside of indications already approved by the U.S. Food and Drug Administration.
"TAPUR is a mechanism to learn about the real-world prescribing of targeted drugs used off-label for the treatment of patients with advanced cancer whose tumors have a genetic profile test performed and reveal some type of genetic abnormality that might be targeted by a targeted anticancer agent," explained Richard L. Schilsky, MD, FACP, ASCO's Chief Medical Officer who has helped lead the charge on launching TAPUR.
This week, the trial (NCT 02693535) launched at 30 clinical sites located in Michigan, North Carolina, South Carolina, and Idaho—with plans to expand further by the end of the year.
"Part of ASCO's mission is to learn from observing the practice of medicine. TAPUR is organized along this philosophy, focusing specifically on the precision medicine space," Schilsky said. "There are two problems—patients often have a test result that indicates a particular drug might be helpful to them, but then they have big problems getting access to the drug because if it's a commercial drug being used off label, it's often not covered by insurance—and drugs are very expensive. And even if the patients are able to get access to the drugs, the other problem is nobody's been able to learn anything from the experience of those patients getting those drugs.
"TAPUR provides us another mechanism to learn how precision medicine is being deployed in practice and whether it's working or not," Schilsky said. "We felt that there was a need an opportunity now."
So how does it work? In a phone interview, Schilsky explained who should enroll and how clinicians can help get their patients on the trial.1. Who should enroll on the trial? Which patients are most likely to benefit?
"The trial eligibility includes all patients with advanced solid tumors, non-Hodgkin lymphoma, or multiple myeloma who have exhausted all standard treatment options or for whom no standard treatment options exist. Those are the patients we're looking for.
"The trial will launch in only a limited number of clinical sites initially. The reason for that is because we need to be sure that all of the trial logistics are performing as they should be—that the data capture measures are all in place, as well as the safety parameters. Our hope is that everything will go smoothly and probably by midyear we'll begin expanding access to the study across the country.
"We have a long list of clinical sites around the country who have already expressed interest in joining the study, and hopefully by the second half of the year, we'll be getting it out there nationally."2. Once the trial expands, how does a cancer center join the trial? And how do physicians enroll their patients?
"All of the participating sites have signed a formal study participation agreement with ASCO to be a study participant site. And they've all received the protocol, which is approved by a central IRB, but the site has the discretion to also submit [the protocol] to their local IRB.
"Then it looks like pretty much any clinical study. There is a two-step eligibility process—patients first need to meet the general eligibility criteria for the study. And then once the specific drug is selected that they'll receive, there is drug-specific criteria that the patient needs to meet.
"The key to enrolling in TAPUR is the treating physician deciding to do a tumor genomic test. In the spirit of having this be a real-world trial that tries to understand what is going on in practice, whether to order a test, which test to order, and which patient specimens to do the test on, is all left to the discretion of the treating physician.
"Once [the physician] has the genomic tests results in hand and a genetic abnormality is identified that matches with one of the drugs available in this study, they can go ahead and register the patient and propose the drug of genomic match they want to use. There's an automated rule in the IT platform of the study that will approve the match or not depending on whether the match comports with the rules in the protocol.
"If the match is approved then the patient can go on and be formally enrolled and receive the treatment. If the match is not approved, then the physician has the option to consult with the TAPUR molecular tumor board, in which case the virtual tumor board would convene by webinar. They'll review the case and identify what they consider to be a reasonable treatment option for the patient. If they agree with the physician's proposal to use of the TAPUR drugs, the patient then can continue in the protocol and receive the treatment.
"The board might recommend a different drug that's in the TAPUR protocol; or if they recommend a drug that's not in TAPUR protocol and the doctor wants to use that option then the patient is no longer followed in the protocol.
"For patients who do enroll, once they start treatment, there's an initial major evaluation that occurs after 16 weeks on the study. The patients can continue on the treatments as long as they have either stable disease or responding disease. Whenever patients have progression they come off that particular treatment."3. When do you expect to report results from the trial?
"It depends how quickly the study enrolls. And there's not a specific number of patients that need to enroll before we can report results. We're going to be reporting results in different groups of patients at different times—by analyzing by cancer diagnosis, the genomic abnormality, and the drug that is used.
"For example if the group was patients with bladder cancer that had a BRAF mutation and got treated with a BRAF inhibitor—that would be a group. And there could be hundreds or thousands of those groups because there are so many genetic abnormalities in tumors.
"In each of those groups we'll initially enroll 10 patients. If there are no responses or only one response, we'll stop enrolling in that particular group. If we see at least two responses, the plan is to enroll 18 additional patients for a total of 28. And for the final analysis, if we see at least seven responses, we'll say that drug has a signal of activity and should be followed up with additional studies or additional data from other sources to confirm that drug has activity in that particular tumor type.
"Whenever we reach those milestones, we'll report out the data—if we close a cohort because of no sign of activity, if we expand a cohort out to 28, etc. I think it's unlikely that we'll have data to report until sometime in 2017."
Wednesday, March 2, 2016
With ERIC DISHMAN, of Intel; and JOE GRAY, PHD, of the Oregon Health and Science University Knight Cancer Institute
The goal is a world where you can capture a patient's whole genome sequence, analyze the data, and determine a treatment for that patient based on his or her genome by the end of that afternoon, Eric Dishman, General Manager of the Health & Life Sciences Group at Intel, as well as Vice President and Intel Fellow of the Data Center Group there, said in a phone interview to discuss the joint effort being spearheaded by Intel and the Oregon Health and Science University Knight Cancer Institute. "We call it the 'All in One Day by 2020' initiative.'"
That goal is the one Brian Druker, MD, Director of the OHSU Knight Cancer Institute, mentioned speaking as part of a panel of experts from academia, industry, and advocacy during a session on the future of data sharing in medicine at the 2015 Partnering for Cures meeting (sponsored by FasterCures, a center of the Milken Institute) (OT 1/10/16 issue). Ears perked up when Druker mentioned an effort was underway (in collaboration with Intel) to make all cancer data shareable by 2020.
The cancer center and tech company together are developing the Collaborative Cancer Cloud—a precision medicine analytics platform that allows institutions to securely share patient genomic, imaging, and clinical data, according to a description on OHSU's website. Dishman, who focuses on the Intel side of the project; and Joe Gray, PhD, Professor and Associate Director of Translational Research for the OHSU Knight Cancer Institute, who is part of the quantitative oncology program at the Knight Institute working on the collaboration, further explained how the platform will work and some of the big challenges they face, in a joint phone interview with OT.
1. What exactly is the Collaborative Cancer Cloud—and how does it work?
GRAY: "What we're imagining is that each institution around the world would have a computer and a data storage capability to manage their data. And each of us would be able to control the data on our site—but if we all agree that we would be able to share that data to answer a particular question, the computer system would allow for me to send a query to your computer; it would run there and then bring the derivative results back to me.
"And this would be done quickly, securely, and cheaply. And it would done in a way that doesn't require that I the analyst actually know how your computer system works. This system that we're developing handles all of that."
DISHMAN: "It's a data-sharing and analytics infrastructure that allows for the sharing of access to data, but without [any one institution] giving up control of it—it allows multiple people to do analysis of the data.
"For example, if I am diagnosed and go to see Dr. Druker at OHSU and he wants to find somebody that looks genetically like me, the chances of him having that data in the OHSU data center are one in a million. Instead, he has to have a huge common denominator of lots of patients' data to say, 'Here's what worked for them, and here's how I can customize a treatment for you based on your genetics.'
"This system—the Collaborative Cancer Cloud—would allow him to send a query to say, 'Hey, anybody else have somebody who looks genetically like Eric?' And instead of them having to give up control of that data or letting him copy and paste all of their data (which you can't do because of HIPPA and security reasons), this system allows that query to be securely sent to an authorized database and bring an answer back to the clinician.
"And the more people you have on that network, the larger that common denominator of data is going to be and the better the chance you have of finding somebody that looks like Eric."
2. What makes this project so challenging? Why is sharing cancer data different from sharing other files over the Internet?
DISHMAN: "First, it's dealing with the large size of the files and moving them around. Many academic medical centers don't even have access to their own cancer data because those files live in the databases of the different departments and can't be shared.
"To put it in perspective—there are about 1.6 to 1.7 million cancer diagnoses expected in the U.S. this year. If we genetically sequenced all those patients' tumors just once it would create four exabytes of data, which is about 400,000 times all of the printed content of the library of Congress.
"And that's just sequencing new cancer patients just once.
"Genome sequencing just for cancer is the largest of the big data challenges that the planet faces."
GRAY: "And even though from a technical point of view—just the engineering of it—the team has already demonstrated proof of concept; the engineers know how to do it. But, to me, the biggest challenge in all of this is how do we get everyone on the same page?
"Once you tell us what to compute on we can do that. But it's defining the standards—getting people to agree what constitutes adequate security for all the data we're trying to manage—that is the biggest challenge. To what extent should we share all of the data? What ethical issues need to be dealt with as we move into the world of sharing a lot of sensitive information about the world's populations?
"These are all what I would call societal problems that the system will enable us to deal with—but we the society actually have to figure out exactly how we want to do it."
3. How is this project different than other big data initiatives, like CancerLinQ, ORIEN?
DISHMAN: "These other efforts are taking a slightly different approach; a lot of them are trying to centralize all the data. They try to bring the big players together who have data and put it all in one central database. All of those efforts create public datasets of genome data for cancer. Intel works on a lot of those efforts.
"But this platform [the Collaborative Cancer Cloud] can stitch all of those different efforts together. So the more of those networks that get created and standardized, the better. [The Collaborative Cancer Cloud] will not replace them. It will create an uber-network that everyone can share from."
GRAY: "The fundamental design of this system is scalability."
DISHMAN: "The Collaborative Cancer Cloud is trying to solve the big data problem now that will work for the research of tomorrow. We think we have a lot of big data now, but it's tiny in comparison to what's coming even two, three, or four years out. Eventually it's going to become too expensive to build a big server farm or database for all of this data—and it can become too difficult for policy and privacy reasons to move all of that data to one central place.
"So how do we connect all of these other efforts—including all of the cancer centers big and small? How do you do that in a safe, secure way where you don't need to be an IT expert to use the system? That's where we're headed with the Collaborative Cancer Cloud."
Friday, February 12, 2016
With MICHAEL KATTAN, PHD, MBA, of Cleveland Clinic and the AJCC's Precision Medicine Core
The current cancer staging system (TNM) was conceived to codify the anatomic extent of disease at diagnosis to provide an accurate prognostic factor for solid tumors—in a way that was simple, easy-to-use, and could be used worldwide. TNM assess cancer progression in patients based on three criteria: the local extent of the cancer within the site of origin (T), the degree of metastatic involvement of the regional lymph nodes (N), and the presence or absence of distant metastatic disease.
But, in the current era of precision medicine, experts say the system is too simple.
So, in 2014 the American Joint Committee on Cancer—the group that developed and maintains the cancer staging system—formed a committee of experts, the Precision Medicine Core (PMC), to develop new criteria to evaluate cancer risk calculators to enhance the current staging system and to determine which ones should be endorsed by the AJCC. Last month that committee published guidelines containing these new criteria, which they say will promote more accurate and individualized cancer predictions, guide more precise treatments, and improve patient survival rates and outcomes. The guidelines are published in a paper online ahead of print in CA: A Cancer Journal for Clinicians (DOI: 10.3322/caac.21339).
The next step will be the various cancer disease management teams of the AJCC reviewing the statistical prediction models that have been created and published in the literature in their respective areas—and making recommendations to the PMC. The PMC will make the final decisions on which models will be endorsed.
In an email interview, PMC member and lead author of the guidelines Michael Kattan, PhD, MBA, Chair of the Department of Quantitative Health Sciences at Cleveland Clinic's Lerner Research Institute, elaborated on why these guidelines are important and why the current staging system needs revamping.
1. What are the limitations of the current cancer staging system?
"It lumps patients within a stage with respect to their prognosis. All patients who are stage II would share the same prognosis—and that's not a very accurate approach. A "bad II" might have a worse prognosis than a "favorable III," but the staging system won't allow this [variability]. A statistical prediction model won't lump patients like that into groups."
2. These new guidelines do not replace the current staging system, right? Could you explain how they will be used?
"Right. The guidelines will permit the addition of statistical prediction models to accompany [cancer] staging systems. The models do not replace or affect the staging systems.
"However, there may come a day when the staging system is no longer useful in the presence of a good statistical prediction model. It is not inconceivable to, at some point, have only statistical prediction models without staging systems because the models predict outcomes more accurately."
3. How are better statistical prediction models and cancer staging systems related to precision medicine?
"All of this essentially means predictions that are better tailored to the individual patient. We move away from considering all patients within a particular stage to be the same. Instead, we take everything we know about you and make the best prediction that we can.
"The next step is reviewing the literature to find existing statistical prediction models that meet requirements. Those [models] will be endorsed. Next we challenge researchers to build more that satisfy our requirements."
Friday, January 29, 2016
With JOHN J. WHYTE, MD, MPH, of FDA’s Center for Drug Evaluation and Research
In oncology, what is the role of race in terms of variability in response to drugs? How many African Americans versus Asians versus Caucasians should be enrolled in a clinical trial? What about age? What about sex?
Those questions form the basis of what would be a very interesting discussion in oncology, explained John J. Whyte, MD, MPH, Director of Professional Affairs and Stakeholder Engagement in the Center for Drug Evaluation and Research of the U.S. Food and Drug Administration. And it was those types of questions that prodded the FDA to launch its new Drug Trials Snapshots online database, he added. “The first step was really just putting all of that information out there.”
For every new molecular entity approved since January 1, 2015 the Snapshots database includes information about who participated in the clinical trials that supported the FDA approval of that drug, highlighting any differences in the benefits and side effects among sex, race, and age groups.
In a phone interview, Whyte elaborated on why the FDA created the online database and what it means for the future of drug development.
1. How did the creation of this database come about?
“In July 2012 President Obama signed the FDA Safety and Innovation Act that required the FDA report to Congress by 2013 on the diversity of participants in clinical trials and the extent to which safety and effectiveness data is based on factors such as sex, age, race, and ethnicity.
“So there has been this effort and directive to be transparent in terms of demographic data in clinical trials. And really it was the vision of the FDA’s Center for Drug Evaluation and Research, Dr. Janet Woodcock, to implement this Drug Trial Snapshots program, a place where anyone can go and find—in an easy-to-read and easy-to-analyze format—all of the participants in clinical trials, based and grouped by sex, race, and age. And anyone can notice any differences in safety and efficacy based on this demographic data.”
2. Is that information all reported in clinical trials anyway? Was all of that information already publicly available?
“The information that’s being captured in Snapshots general existed in different documents and databases that were available to the public, but it might have been hard to find.
“For instance, all of the Medical Officer Reviews for new molecular entities are on the [FDA] website, but those data might be in very lengthy documents. So now the Drug Trials Snapshot provides you that information in text and in pictorial format that may not have existed elsewhere.
3. How do you expect the database to be used? Will it change physicians’ day-to-day practice in terms of what drugs they prescribe to patients?
“This [initiative] is about transparency. There have been a lot of advocacy groups that have said the number of women in all clinical trials should be 50 percent, or that the number of African Americans should be proportionate to prevalence of the disease in the population. What is the right number of people in clinical trials to make statements about safety and efficacy based on sex, race, and age? The first step is really putting all of that information out there.
“You’re asking—should we change our actions because there aren’t enough Asians enrolled in a trial for breast cancer or thyroid cancer? We’re not saying that. We’re saying here is one more piece of information that you want to consider. This information is intended to spur the debate and the discussion about what is the right number of people.
“And we know we likely do need to enroll more women and more African Americans in clinical trials. But, what we’re really interested in right now is to what endpoint? How do we know we’ve reached the right number?
“We’re saying let’s continue to study it to find out what is the right number. And in the meantime physicians and patients should look at this information and take it in the broader context of does it matter if only two percent of a trials participants were African Americans. It’s not a simple answer.”