How the Value of Data is Upending Traditional Business Models in Oncology

It’s 1990: ten years before the initial sequencing of the Human Genome Project was completed. Only ten years after the first clinical use of the MRI and still more than a decade before the Health Insurance Portability and Accountability Act (HIPAA) would be enacted. Electronic medical records (EMRs) wouldn’t be widely discussed until President G.W. Bush’s 2004 State of the Union address. The top cancer hospitals were University of Texas MD Anderson Cancer Center and Memorial Sloan-Kettering Cancer Center [1]. Patients seeking care at these or other top medical centers had to navigate a Byzantine process, sometimes lugging suitcases of medical records from their local hospital to a specialized cancer center and subjecting themselves to repeat diagnostic testing. Without a comprehensive EMR system, individual researchers often used offline files, like Microsoft Excel databases, to keep track of their patients’ outcomes and other key data. Sharing this data was practically nonexistent. Matching a patient to a clinical trial was difficult and influenced substantially by where the patient received treatment. Quality of care was haphazard. Simply put, hospitals and oncologists didn’t have the IT infrastructure needed to better compare patients, track their treatments and outcomes, and assign them quickly to clinical trials [2].

Fast forward nearly three decades later and process looks virtually identical in some ways but has improved light-years in others. Precision Medicine is no longer just a buzzword touted for marketing purposes, but an actionable way to better treat some cancer patients based on molecular biomarkers and therapeutics designed to target specific genomic mutations. Along the way, new businesses (and business models) have sprung up to fill glaring unmet needs for the patients, providers, and even the pharmaceutical companies developing new treatments. The underlying theme behind these new companies? Data and technology.

Aggregating Patient Data for Better Outcomes

Flatiron Health

While oncologists still maintain databases of patient information for research, this data has begun to migrate from offline databases and paper-and-pencil patient forms to HIPAA-compliant, industry- and federally-funded maintained sites like dbGaP and is slowly becoming integrated within electronic health records to inform patient care. Flatiron Health is one example of a company that has succeeded in this transition. Launched in 2012, Flatiron was created in response to the fragmented U.S. healthcare system. Founders Nat Turner and Zach Weinberg saw first-hand the negative impact data silo-ing had on family members’ cancer care and aimed to fix it [3].

Flatiron developed an EMR system for community oncologists to help them treat their patients more efficiently and effectively, giving providers actionable insights from clinical and business data. In the 6 years since the start of Flatiron, the company has partnered with more than 265 community-based oncology clinics and six academic research centers [4]. They’ve also developed relationships with diagnostics companies like Foundation Medicine to build a database of both clinical and genomic data [5]and with the National Comprehensive Cancer Network (NCCN) to embed NCCN chemotherapy templates within its EMR product [6]. Through these partnerships, Flatiron and their colleagues are performing complex data analytics, leveraging the patient data from the company’s EMR tools.

The clinical impact of Flatiron can be seen in the numerous scientific publications arising from use of its data, including a recent study by researchers at Eli Lilly that measured the amount of drug waste in patients with non-small cell lung carcinoma [7]and another by scientists at Amgen who evaluated treatment patterns of patients with multiple myeloma in real-world (i.e., non-clinical trial) settings [8].

Cota Healthcare

Flatiron isn’t the only company to have taken a data-centric approach to oncology care. New York-based Cota Healthcare, founded in 2011, assigns patients a “Cota Nodal Address” (CNA) in order to group patients with similar attributes[9]. The idea is to group patients with similar clinical characteristics so that differences in treatments can be more easily compared [10]. Data analytics and real-world data figure prominently in Cota’s business model, which offers solutions for payers and providers, as well as researchers.

As with Flatiron, Cota has made a point of publishing studies of interest to providers, including the development and validation of a patient-reported outcome screening tool for end-of-life discussions [11]. Other topics researchers at Cota have presented at scientific conferences include patient perspectives on the financial implications for advanced cancer care [12]and the use of Cota’s digital classification system to identify treatment variation and support value-based payment reforms [13].

Cota’s patented CNA system is valuable for Precision Medicine initiatives, which strive to identify biomarkers and other factors that determine a patient’s prognosis or how they will respond to a particular treatment in a real-world setting. This has led to a key partnership with Novartis Pharmaceuticals [14]and several hospitals including Memorial Sloan Kettering Cancer Center [15]and Hackensack Meridian Health [16]. Hackensack Meridian Health is currently piloting a program using Cota and IBM Watson products to tackle cancer care [17].

Tempus

Tempus, a Chicago-based health technology company founded by Eric Lefkofsky, a co-founder of Groupon, is another example. Tempus, which reached unicorn status after a recent funding round [18], has partnered with numerous hospitals and cancer centers across the country to create a de-identified database of patient information that participating providers can access. This data can be used by doctors to help guide personalized treatment for individual patients [19]. A key difference between Tempus and Flatiron is that Tempus also offers sequencing as a service; putting genomic data together with clinical data is driving Precision Medicine forward, whereas Flatiron partners with other companies to obtain this data. This structure benefits not only the treating physicians and their patients, who gain from the insights gleaned from Tempus’ database, but also Tempus, which can use the sequencing and clinical data to identify biomarkers which could be commercialized in the future as in vitrodiagnostics [20].

Winning Value Propositions

Flatiron, Tempus, and Cota have garnered substantial business success with their platforms, namely the packaging and analytics of patient data for oncology. Early investors in Flatiron, like Roche, helped propel the company from startup to unicorn status in 4 years, with experts valuing the company at more than $1.2 billion [21, 22]before its acquisition in early 2018 by Roche [23]. An $80 million funding round in March helped to propel Tempus to unicorn status [24]three years after its founding by Lefkofsky. To date, Cota hasn’t reached unicorn status (valuation in excess of $1 billion) but has nabbed $65 million in four funding rounds. The latest of these, a $40 million Series C round, was led by IQVIA, a healthcare and technology company with additional participation by Memorial Sloan Kettering Cancer Center and EW Healthcare Partners, an oncology-focused pharmaceutical company [25].

One of the keys to their success is unlocking the potential of massive amounts of patient data that has been previously locked away in small databases or file drawers. While doctors and hospitals are natural consumers for solutions that these companies provide, pharmaceutical companies are interested as well. Roche was a key investor in Flatiron even before the acquisition and Cota has a pharma-facing arm of its tech solution [9]. Tempus’ combined sequencing and clinical data make it a valuable partner for pharmaceutical and diagnostics companies developing companion diagnostics and new therapeutics [26]. In December, they beat out competitors Flatiron and IQVIA to license the American Society of Clinical Oncology’s CancerLinQ database of treatment results and structure the data meaningfully for oncologists and researchers [9, 27].

Health Centers Compete with Startups

While numerous startups have found a niche offering platforms to aggregate and structure patient clinical and sequencing information, health systems are beginning to catch on to the potential advantages of commercializing their data. Many academic medical centers have created biobanks of patient samples which are linked to the EHR or a de-identified version of the EHR for clinical research at the institution, including places like Geisinger Health and Kaiser Permanente [28, 29]. But most of these biobanks are restricted for use by their clinical researchers or scientists within consortia of similar medical centers.

In April, Nashville’s Vanderbilt University Medical Center took an unusual approach by spinning out a subsidiary, Nashville Biosciences, to serve as a link between the health center and commercial entities [30, 31]. Like other medical centers that have been at the forefront of Precision Medicine, Vanderbilt has an extensive electronic medical records systems that is linked to a biobank, called BioVU. This data is a veritable goldmine for companies looking to develop novel molecular diagnostics or repurpose pharmaceuticals for new indications. Diagnostics companies and pharmaceutical companies, like Population Bio, are among the new entity’s target audience, which is building on previous relationships with Pfizer and Celgene. Through their partnership with Nashville Biosciences, Population Bio, previously known as Population Diagnostics, may have a competitive advantage to others in the industry, as they make use of the large amount of clinical and genomic data in BioVU.  In addition to BioVU, the medical center’s biobank and EHR data repository, Nashville Biosciences offers the analytical know-how to work with the data.

Paying Patients for their Data

Currently, patients aren’t reimbursed when they allow their hospitals to collect their samples for a biobank or when their health information (e.g. lab values, outcomes) are used in clinical research. Direct-to-consumer companies, such as 23andMe, ask their customers for additional health data they can use in research studies and both for-profit and non-profit purposes. But as the previous examples demonstrate, that patient data is incredibly valuable, and companies are willing to pay for access.

A few new startups are looking to turn that business model on its head by offering to pay consumers for their data. Genos, a spinout from Complete Genomics in 2016, offers to sequence consumers’ DNA for a price and customers can then sell their data to academic and industry researchers using their research pipeline. Over time, sharing their data with researchers might net a customer more than the initial cost of sequencing [32]. Nebula Genomics offers consumers a similar experience: pay $1000 to have your genome sequenced, and Nebula will put it on a blockchain where you control access and what is done with it while you earn cryptocurrency [33]. Founded by George Church, a genetics guru from Harvard University, Nebula is unusual in leveraging the privacy and security of blockchain for genetic data [34]. Why might this appeal more than 23andMe’s or Color Genomics’ much cheaper tests? Genos and Nebula are betting that letting consumers have control over their data and getting paid to grant permission to researchers to use their data will appeal to many.

What’s to Come

With the emphasis on Precision Medicine in oncology, the value of clinical data has never been higher and the potential to create the next Google in healthcare has never been stronger. Oncologists now can have access to databases filled with patient outcomes data that are far larger than any they would have accumulated in their practice, so they can determine the best treatment for an individual patient. But this data isn’t being confined to academic research centers—growing numbers of for-profit companies are accessing and licensing this data, restructuring it, and combining it with data from other streams, creating datasets that work not only for doctors, but also for diagnostics developers and pharmaceutical companies. The end result? A new paradigm based on the value of data and the insights it provides.

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