Jan 19 2016

Cancer ‘Moonshot’ needs Informatics

by at 10:33 am

Many of us who work in the interface of Cancer Clinical Research and Biomedical informatics were thrilled to hear about the cancer moonshot program from President Obama announced in his final State of the Union Address on Tuesday, Jan 12’16.

VP Biden, the nominated leader for this effort, has pledged to increase the resources available to combat the disease, and to find ways for the cancer community to work together and share information, the operative word being “share” (after ‘resources’).

In this post, I briefly review (by no means comprehensive; just a Saturday morning project while brunch cooks in the Instant pot) four thematic areas where informatics is already playing a key role to help realize cancer moonshot goals and identify challenges and opportunities.

  • Immunotherapies: Recent approvals of ipilimumab (Yervoy), sipuleucel-T (Provenge), Nivolmab (Opdivo) and Pembrolizumab (Keytruda) represent important clinical advances for the field of active immunotherapy in oncology and for patients with melanoma and prostate cancer, respectively. Immunoinformatics has played a critical role in B- and T- cell epitope prediction during the course of development of these therapies. New predictive computational models to describe the time-dependent relationships of cancer, immunity, and immunotherapies have emerged over the last few years. Using next gen sequencing approaches such as whole genome, exome and RNA sequencing, it is now possible to characterize with high accuracy the individual set of Human Lymphocyte Antigen (HLA) alleles of an individual patient leading to personalized immunotherapies. The biggest challenge in immunoinformatics arises from the routine sequencing of individual human genomes. We need new informatics tools to study the impact of natural genomic variation on the immune system and how to tap into it for new therapies. Click here for further reading.
  • Precision medicine: President Obama’s precision medicine initiative and the $215M investment have brought precision medicine to the forefront of many organizations. The cost of cancer care is estimated at $200 Billion each year and only on the rise as our population increases and lives longer. Many pundits see Precision Medicine as a way to deliver value-based cancer care. Thanks to high throughput technology, including genomic testing of each tumor, and each patient’s inherited DNA— along with proteomics in the future—oncologists are able to tailor regimens for gene mutations in each patient thus avoiding high cost of drugs that may not work. A key informatics challenge is to figure out which of the thousands of mutations in a patient’s tumor are drivers or actionable markers. There is a race in both academic and commercial space to develop software that will tease out the ‘drivers’ from the ‘passengers’. Furthermore, mutations have to be categorized by levels of evidence: high evidence – where the gene mutation has been tested in a randomized controlled trial (RCT) setting, medium evidence – retrospective gene mutation analysis of RCTs- and finally low level evidence with pre-clinical data only on the mutation. We need better evidence modeling approaches to categorize actionable mutations if clinicians are to use these in routine patient care. Click here for further reading.
  • Cell free DNA/blood tests: While molecular profiling in solid tumors remains routine practice in cancer diagnostics, modern technologies have enabled detection of biomarkers in stray cells, exosomes and traces of DNA in blood and other body fluids. This offers a low cost method to obtain cancer-profiling data for diagnosis and treatment when invasive tissue biopsies may be clinically difficult. While technologies and informatics methods for detecting very small amounts of tumor DNA are on the rise, there are many biological issues that need to be addressed. If the tumor cell did not shed a single piece of variant DNA, even the most sensitive technology will be unable to detect it. Commercial interest in this space is enormous. The Genomics/Informatics Company Illumina has just launched a new startup, GRAIL, in collaboration with Jeff Bezos and Bill Gates to develop a single blood test that could detect cancer early. Now, that is a moonshot goal! Click here for further reading.
  • Organizing cancer data: Now on to my favorite topic of organizing cancer data to power new discovery. Secondary use of EHR data for observational studies is improving through clinical research networks. As large biorepositories linked to electronic health records become more common, informatics is enabling researchers to identify cohorts that meet study criteria and have requisite consents.
    Modified from Thomas Wilckens, MD

    Modified from Thomas Wilckens, MD

    While there have been significant efforts in sharing molecular data sets publicly, less progress has been made on sharing healthcare data. Many standards exist today to facilitate data sharing and interoperability. We need more training of existing standards to consumers (app developers, scientists) of standards. We also need a comprehensive knowledgebase ecosystem that supports federated queries across cancer subtypes, risk, molecular features, diagnosis, therapy and outcomes at an individual level to advance biomarker discovery and better clinical decision support. Real-world Big Data on claims, outcomes, drug labels, research publications, clinical trials are now available and ready to be linked and analyzed to develop better cancer treatments. NCI’s TCGA and Rembrandt, Georgetown Lombardi Cancer Center’s G-DOC, Global Alliance for Genomic Health (GA4GH), ASCO’s CancerLinQ are all efforts in this direction. Let’s unleash cancer big data in effective ways to collectively make the moonshot program a reality! Click here for further reading.

Programs such as the cancer moonshot are a journey, not a destination and if directed appropriately, can inevitably better the practice of cancer medicine.

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Nov 03 2015

Quantified Self, GenomeFirst and a trip to the White House – A busy week for Biomedical informatics

by at 11:22 pm

“You are over pronating!” bleeps Bluetooth enabled, sensor-infused smart socks just 10 minutes into your daily running routine.

Or, “check your blood pressure, temperature and heart rate using Scanadusm, with no cuffs, in just 15 seconds!” quips Kevin Maloy of MedStar Institute for Innovation during our fourth annual Georgetown Biomedical Informatics Symposium on October 16, organized and hosted by the Innovation Center for Biomedical Informatics. Emerging trends in informatics and health IT were demonstrated and discussed with over 350 attendees from academia, industry and government. The event benefited from strong support of institutional and industry sponsors. Find out more about the symposium’2015 here. I present Cliff Notes version of four major themes here for your quick browsing.

  1. Quantified Self

We are increasingly wearing bands to track how much we move, strapping on watches to listen to our heartbeat, and logging what we eat and drink. The underlying proposal is that describing ourselves with these numbers will put healthcare back in the hands of people. Will the “quantified self” become a major driver in how diseases are prevented or treated? This is one of the intriguing questions that our symposium explored.

Informatics opportunity: Design in healthcare is an opportunity to improve signal and reduce noise in a system that is over stretched, under utilized and very expensive.

  1. EHRs and other emerging health technologies

Digitized health is a dream come true for many. But are electronic health records (EHRs) actually getting in the way of physician productivity? At our symposium, Mike Hogarth of University of California, Davis presented results from a survey of 410 internists that estimated that 42 minutes are lost each day by physicians due to EHRs. About 80% of key clinical data are in the form of unstructured narratives – a mess he referred to “dirta,” instead of data. This information requires enormous quality control, structuring, and integration – a reality that raises the question: can practice-based evidence be generated through retrospective studies of EHR datasets?

Informatics opportunity: Nigam Shah of Stanford University suggested that enterprise wide data governance at hospital systems, or a green button function within EHRs, could help clinicians use aggregate patient data to support decisions at the point of care. Ben Schneiderman of University of Maryland demo’ed EventFlow, a tool for visual analysis of temporal events within patient records to enable advanced healthcare discovery. Zak Kohane of Harvard University, in his keynote lecture, cited clinical research data integration software such as i2b2, tranSMART, and SMART Health IT apps as solutions to the “dirta” problem in healthcare innovation.

  1. Trends in Precision Medicine

A lot of the excitement at the symposium – amplified by the talks on targeted therapies in pancreatic cancers and a panel discussion on Next Generation Sequencing (NGS) in the clinic – was focused on Precision Medicine.

Mike Pishvaian of Georgetown University and Dr. Jon Brody of TJU discussed PANCan’s “Know your tumor” program. This program has found that 43% of patients had actionable findings from molecular profiling, resulting in modified treatment recommendations and better responses.

Regeneron’s Rick Dewey asked a provocative question: what if everybody’s genome was available in his or her medical record? Rick and Marc Williams of Geisinger described a collaboration between Regeneron and Geisinger to use EHRs and exome sequencing data from over 200,000 individuals for faster drug discovery. It was a treat to hearabout Geisinger’s GenomeFirst initiative, which is implementing genome inference engines – clinical decision support and predictive models to enable Precision Medicine in a unique way with teams of clinicians, genetic counselors, nurse practitioners and informaticians.

No scientific symposium is complete without an award! The (iPAD winning) best poster award went to Ao Yuan, graduate student in Biostatistics at GU for his work on a semi parametric model for the analysis of patient sub-groups in precision medicine trials.

The Precision Medicine journey is underway, and is already improving medicine. Informaticians are vital to this journey. More work is needed to collect the right patient cohorts for research, to identify the right markers to test, and to develop the appropriate targeted therapies.

The Symposium explored what’s next for all of us in this important journey?

Informatics opportunity: Curating evidence of biomarker association with drug response, novel data wrangling approaches to extract and analyze useful clinical and genomic data to drive new hypothesis generation and clinical decision support, and data science approaches to connect genotypes to phenotypes are a few of many opportunities for informaticians to meaningfully participate in the precision medicine revolution.

  1. Security, Privacy and Trust principles for patient-centered studies

The symposium was a perfect lead-in to a great roundtable discussion on a much-needed security framework for President Obama’s Precision Medicine Initiative at the White House OSTP. I was humbled by the discussion with experts in cyber security, patient privacy, trust principles, and data breach. Will “white hat hacking” help? How can we use it in the context of protecting healthcare data and participants from willful misuse?

Informatics opportunity: DJ Patil, US Chief Data Science Officer emphasized the need for IT teams to focus on data infrastructure, auditing and monitoring of patient genomic data, data transmission and access infrastructure, including tiered data access.

It is so gratifying to see informaticians providing thought leadership across the full spectrum of clinical research and care. Let’s continue the conversation – find me on e-mail at subha.madhavan@georgetown.edu or on twitter at @subhamadhavan.

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Aug 07 2015

Practical Precision Medicine: Striving for Better Medicine

by at 12:33 pm

Practical Precision Medicine is about striving for better medicine. But it means different things to different people.

For patients, it promises fewer “trial and error” therapies and fewer side effects, especially fatal ones. The New England Journal reported the tragic case of a 2-year old boy with obstructive sleep apnea who underwent a routine, outpatient adenotonsillectomy. After an uncomplicated surgery, the parents were sent home with a prescription for acetaminophen with codeine. Unknown to the physicians, he had a functional duplication of the CYP2D6 allele, the enzyme that turns codeine into morphine. Practically, this resulted in a lethal dose of morphine in his blood. If a genetic test for this were available in the right place, at the right time, could it have prevented this tragedy?

For patients, practical precision medicine also means new therapies and hope.

Case in point, the remarkable story of one woman’s ordeal with pancreatic cancer detailed at Georgetown’s Lombardi Cancer Center website. When standard chemotherapy failed, genetic testing identified an experimental therapy (PARP inhibitor) that made her cancer disappear.

For providers, Precision Medicine is somewhat of a mixed bag. Some of this genetic testing is old news. For years they have been testing for Prothrombin mutationFactor V Leiden or HIV 1 genotype. In this case, it is not called Precision Medicine; it is simply called routine clinical practice.

The challenge for clinicians lies in the evidence that Precision Medicine directly improves outcomes. Examples of therapies that made sense, were widely used, and then proved harmful (for example hormone replacement therapy to prevent first heart attack or stroke) litter the history of medicine. There are always more tests to order. However, the issue is determining which tests pass beyond the standard of “it makes sense it should work” and actually improve outcomes when studied in a rigorous manner.

The Precision Medicine journey has already begun, meaning different things to different individuals but inevitably bettering the practice of medicine.

By Subha Madhavan, PhD & Kevin Maloy, MD, 2015

*Post originally appeared on MedStar Institute for Innovation (MI2) blog site 

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Jun 14 2015

Health Datapalooza ’15

by at 12:30 pm

It was a treat to all data enthusiasts alike! What started out five years ago with an enlightened group of 25 gathered in an obscure forum has morphed into Health Datapalooza which brought 2000 technology experts, entrepreneurs and policy makers and healthcare system experts in Washington DC last week. “It is an opportunity to transform our health care system in unprecedented ways,” said HHS Secretary Burwell during one of the keynote sessions to mark the influence that the datapalooza has had on innovation and policy in our healthcare system. Below are my notes from the 3-day event.

Fireside chats with national and international leaders in healthcare and data science were a major attraction. Uhealthdatapalloza.S. Chief Data Scientist DJ Patil discussed the dramatic democratization of health data access. He emphasized that his team’s mission is to responsibly unleash the power of data for the benefit of the American public and maximize the nation’s return of its investment on data. Along with Jeff Hammerbacher, DJ is credited to have coined the term data science. Most recently, DJ has held key positions at LinkedIn, Skype, PayPal and eBay. In Silicon Valley style, he said that he and his team are building a data product spec for Precision Medicine to drive user-centered design, he quoted an example of such an app, which will provide allergy-specific personalized weather based recommendations to users. Health meets Climate!

Responsible and secure data sharing of health data is not just a “nice to have” but is becoming a necessity to drive innovation in healthcare. Dr. Karen DeSalvo, the Acting Assistant Secretary for Health in the U.S. Department of Health and Human Services, is a physician who has focused her career toward improving access to affordable, high quality care for all people, especially vulnerable populations, and promoting overall health. She highlighted the report on Health information blocking produced by the ONC in response to Congress’s request. As more fully defined in this report, information blocking of electronic healthcare data occurs when persons or entities knowingly and unreasonably interfere with the exchange or use of electronic health information. The report produced in April lays out a comprehensive strategy to address this issue. She also described early successes of mining of social media data for healthcare describing the use of Twitter to predict Ebola outbreak. Lastly, she shared a new partnership between HHS and CVS on a tool that will provide personalized, preventive care recommendations based on the expert recommendations that drive the MyHealthFinder, a tool to get personalized health recommendations.

There was no shortage of exciting announcements including Todd Park’s call for talent by the U.S. Digital Service to work on the Government’s most pressing data and technology problems. Todd is a technology advisor to the White House based in Silicon Valley. He discussed how the USDS teams are working on problems that matter most – better healthcare for Veterans, proper use of electronic health records and data coordination for Ebola response.  Farzad Mostashari, Former National Coordinator for Health IT, announced the new petition to Get my Health Data – to garner support for easy electronic access to health data for patients. Aaron Levine, CEO of Box described the new “platform” model at Box to store and share secure, HIPAA-compliant content through any device. Current platform partners include Eli Lily, Georgetown University and Toyota among others.

An innovative company and site ClearHealthCosts, run by Jeanne Pinder, a former New York Times reporter for 23 years, caught my attention among software product demos. Her team’s mission is to expose pricing disparities as people shop for healthcare. She described numerous patient stories including one who paid $3200 for an MRI. They catalog health care costs through a crowdsourcing approach with patients entering data from their Explanation of benefit statements as well as form providers and other databases. Their motto – “Patients who know more about the costs of medical care will be better consumers.”

Will the #hdpalooza and other open data movements help improve health and healthcare? Only time will tell but I am an eternal optimist, more so after the exciting events last week. If you are interested in data science, informatics and Precision Medicine don’t forget to register for the 4th annual ICBI Symposium on October 16. More information can be found in this Newsletter. Let’s continue the conversation – find me on e-mail at subha.madhavan@georgetown.edu or on twitter at @subhamadhavan

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Feb 13 2015

Informaticians on the “Precision Medicine” Team

by at 8:34 am

My first recollection of the term “Precision Medicine” (PM) is from a talk by Harvard Business School’s Clayton Christensen on disruptive technologies in healthcare and personalized medicine in 2008. He contrasted precision medicine with intuitive medicine, saying, “the advent of molecular diagnostics enables precision medicine by allowing physicians to delineate conditions that are likely constellations of diseases presenting with a handful of symptoms.” The term became mainstay after NRC’s report, “Toward precision medicine: Building a knowledge network for biomedical research and a new taxonomy of disease.” Now, we converge on the NIH’s definition– PM is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle.

“Cures for major diseases including cancer are within our reach if only we have the will to work together and find them.  Precision medicine will be the way forward,” says Dr. John Marshall, head of GI Oncology at MedStar Georgetown University Hospital.

The main question in my mind is: How can we apply PM to improve health and lower cost? Many sectors/organizations are buzzing with activity around PM to help answer this question.

NIH is developing focused efforts in cancer to explain drug resistance, genomic heterogeneity of tumors, monitoring outcomes and recurrence and applying that knowledge in the development of more effective approaches to cancer treatment. In a recent NEJM article, Drs. Collins and Varmus describe NIH’s near-term plan for PM in cancer and a longer-term goal to generate knowledge that is broadly applicable to other diseases (e.g., inherited genetic disorders and infectious diseases). These plans include an extensive characterization and integration of health records, behavioral, protein, metabolite, DNA, and RNA data from a longitudinal cohort of 1 million participants. The cost for the longitudinal cohort is roughly $200M to expand trials of genetically tailored treatments, explore cancer biology, and set up a “cancer knowledge network” for sharing this information with researchers and oncologists.

FDA is working with the scientific community to ensure that the public can be confident that genomic testing technology is safe and effective while preserving innovation among developers. The FDA recently issued draft guidance for a framework to regulate laboratory-developed tests (LDTs). Until now, most genomic testing is done through internal custom developed assays or commercially available LDTs. The comment period just ended on Feb 2.

Pharma/Biotech companies are working to discover and develop medicines and vaccines to deliver superior outcomes for their customers (patients) by integrating “Big Data” (clinical, molecular, multi-omics including epigenetics, environmental, and behavioral information).

Providers, health systems, and Academic Medical Centers are incorporating appropriate molecular testing in the care continuum and actively participating in clinical guideline development for PM testing and use.

Public and private Payors are working to appropriately determine clinical utility, value and efficacy of testing to determine reimbursement levels for molecular diagnostic tests – a big impediment for PM testing right now. Payors recognize that collecting outcomes data is key to determining clinical utility and developing appropriate coding and payment schedule.

Diagnostic companies are developing and validating new diagnostics to enable PM, especially capitalizing on the new value-based reimbursement policies for drugs. They are also addressing joint DX/RX approval processes with the FDA.

Professional organizations are setting standards and guidelines for proper use of “omics” tests in a clinical setting – examples include AMA’s CPT codes, ASCO’s QOPI guidelines, or NCCN’s compendium.

Many technology startups are disrupting current models in targeted drug development and individualized patient care to deliver on the promise of PM. mHealth domain is rapidly expanding with innovative mobile sensors and wearable technologies for personal medical data collection and intervention.

As informaticians and data scientists, we have atremendous opportunity to collaborate with these stakeholders to contribute in unique ways to PM:

  1. Develop improved decision support to assist physicians in taking action based on genomic tests.
  2. Develop common data standards for molecular testing and interpretation
  3. Develop methods and systems to protecting patient privacy and prevent genetic discrimination
  4. Develop new technologies for measurement, analysis, and visualization
  5. Gather evidence for clinical utility of PM tests to guide decisions on utility
  6. Develop reference databases on the molecular status in health and disease
  7. Develop new paradigms for clinical trials (N of one trials, basket trials, adaptive designs, other)
  8. Develop methods to bin patients by mutations and pathway activation rather than by tissue site alone.
  9. Create value from Big Data

What are your ideas? What else belongs on this list?

Jessie Tenenbaum, Chair, AMIA Genomics and Translational Bioinformatics shares: “It’s an exciting time for informatics, and translational bioinformatics in particular. New methods and approaches are needed to support precision medicine across the translational spectrum, from the discovery of actionable molecular biomarkers, to the efficient and effective storage and exchange of that information, to user-friendly decision support at the point of care.”

A PricewaterhouseCoopers analysis predicts the total market size of PM to hit between $344B-$452B in 2015. This includes products and services in molecular diagnostics, nutrition and wellness, decision support systems, targeted therapeutics and many others. For our part, at ICBI, we continue to develop tools and systems to accurately capture, process, analyze, and visualize data at patient, study, and population levels within the Georgetown Database of Cancer (G-DOC). “Precision medicine has been a focus at Lombardi for years, as evidenced by our development of the G-DOC, which has now evolved into G-DOC Plus. By creating integrated clinical and molecular databases we aim to incorporate all relevant data that will inform the care of patients,” commented Dr. Lou Weiner, Director, Lombardi Comprehensive Cancer Center who was invited to the White House precision medicine rollout event on January 30.

Other ICBI efforts go beyond our work with Lombardi. With health policy experts at theMcCourt School of Public Policy, we are working to identify barriers to implementation of precision medicine for various stakeholders including providers, LDT developers, and carriers. Through our collaboration with PRSM, the regulatory science program at Georgetown, and the FDA, we are cataloging SNP population frequencies in world populations for various drug targets to determine broad usefulness of new drugs. And through theClinGen effort, we are adding standardized, clinically actionable information to variant databases.

The President’s recent announcements on precision medicine have raised awareness and prompted smart minds to think deeply about how PM will improve health and lower cost. We are one step closer to realizing the vision laid out by Christensen’s talk in 2008. ICBI is ready for what’s next.

Let’s continue the conversation – find me on e-mail at subha.madhavan@georgetown.edu or on twitter at @subhamadhavan

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Oct 24 2013

Keynote Talks at ICBI symposium: Stephen Friend and Eric Hoffman

by at 4:40 pm

Big Data in Precision Medicine was the focus of the 2nd Annual Biomedical Informatics Symposium at Georgetown, which drew nearly 250 people to hear about topics from direct-to-consumer (DTC) testing to mining data from Twitter.

The morning plenary on Genomics and Translational Medicine was kicked off by Stephen Friend, MD, PhD, President, Co-founder, and Director of Sage Bionetworks who discussed the “discontinuity between the state of our institutions and the state of our technology.”   This disconnect stems from the way results are presented in the literature and compared with one another in different scenarios, and sometimes interpreted into the clinic. “We are going to get different answers at the DNA, RNA, and functional levels,” said Friend, and different groups working on the same data can get different answers because science “context dependent” – dependent on the samples, technologies, and statistical parameters.  Our minds are wired for a “2D narrative” but the fact is we are all just “alchemists.”

Friend is a champion of open data sharing and turning the current system on its head.  We need “millions of eyes looking at biomedical data…not just one group, it’s immoral to do so,” Friend said.  We need to get rid of the paradigm, “I can’t tell you because I haven’t published yet.”   He said that GitHub has over 4M people sharing code with version tracking, and in fact hiring managers for software engineering jobs are more likely to look for a potential candidate’s work on GitHub than to considering credentials on a CV.

Sage created Synapse, a collaborative and open platform for data sharing, which he hopes could be the GitHub for biomedical scientists.   He would like to see large communities of scientists worldwide working together on a particular problem and sharing data in real time. As an example of this sort of effort, check out the Sage Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge.  His excitement for this future model for large scale collaboration was palpable in his closing remarks—a prediction for a future Nobel prize for “theoretical medicine.”

The afternoon plenary on Big Data in Biomedicine was led by a keynote talk from Eric Hoffman, PhD, Director of the Research Center for Genetic Medicine at Children’s National Medical Center who discussed “data integration in systems biology”  -which is a topic very close to the heart of ICBI.  He presented a new tool, miRNAVis, to integrate and visualize microRNA and mRNA expression data, which he referred to as “vertical” data integration or the integration of heterogeneous data types.  This tool will soon be released for public use.

Hoffman is considered one of the top world experts in muscular dystrophy research, having cloned the dystrophin gene in Louis Kunkel’s lab in 1987.  He has made an enormous contribution to research in this field along with dedicating countless hours to volunteering with children affected by the horrible disease.  He discussed a very exciting project in his lab on a promising new drug – VBP15, which has anti-inflammatory properties, and shows strong inhibition of NF-κB, and repair of skeletal muscle.  Most importantly, VBP15 does not have the side effects of glucocorticoids, which are currently the standard treatment for Duchenne muscular dystrophy. Hoffman said this new drug may potentially be effective against other chronic inflammatory diseases.  Let’s hope this drug will make it into clinical trial testing very soon!

More information about the keynote and other talks can be found on ICBI’s Twitter feed and at #GUinformatics, which provided snapshots of the day.

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