How would you feel if, after years of searching for a diagnosis you finally found out you have an autoimmune disease, and then you realize that your doctors will have to experiment on you to find the right treatment?
That’s the state of the art today in autoimmune diseases like Crohn’s, lupus, and MS.
At least 50 million Americans (twice the number of cancer patients) suffer from autoimmune diseases. Each of the 90 or more named diseases is represented by a variety of stakeholders, (patients, specialists, researchers), with little sharing of data across groups, and even less across diseases.
Cancer (and rare disease) communities are already moving to collect, share and derive useful information from data. However, despite the similarities (such as unmet needs, heterogeneous diseases, poor diagnostics), the “autoimmune communities” lag some 50 years behind cancer R&D in data collection, sharing and analysis, and therefore, improved diagnoses and treatment.
Why have autoimmune diseases been neglected?
- Like cancer, autoimmune diseases are multifactorial and heterogeneous, and we have little understanding of the contributions of genetic, epigenetic, cell biology, metabolic, microbiological and environmental factors.
- Drug development has been difficult, with a history of repeated failures (e.g., lupus) and the need for multiple attempts to succeed (psoriasis, RA, MS).
- Even worse than cancer, the autoimmune clinical landscape is extremely siloed, with multiple specialists (gastroenterologists, dermatologists, rheumatologists) and very few clinical immunologists.
- Diagnosis is difficult, with vague symptoms that wax and wane, and undereducated clinicians likely to dismiss patients as hypochondriacs.
- Where we do have FDA-approved treatments (psoriasis, RA and MS), we lack protocols for matching patients to drugs that differ in mechanism of action, effectiveness and cost; thus clinicians tend to try the cheapest drug first.
If big data (and technology) have helped in cancer and rare diseases, can they also help in autoimmune diseases, too? How can data-driven research in –omics and immunology help improve prevention, diagnosis, prognosis and management?
We need to collect data, and share it with those who can make it useful. How do we do this?
- Acquisition: What if we had initiatives like Genetic Alliance, or PatientsLikeMe for autoimmune patients to collect and donate data?
- Sharing: Can we scale efforts like TD1exchange, Smart Patients and Crohnology across the growing spectrum of autoimmune diseases?
- Analysis: Can we apply big data approaches (Humedica or Flatiron) or pattern recognition (like Ayasdi and GNS) to redefine understanding of the autoimmune disease spectrum? Perhaps there are more than two types of diabetes. Could crowd-sourced clinical trials improve drug development? Can crowds improve the success in difficult cases such as those in CrowdMed? Could competitions such as Kaggle help sort out treatment protocols?
What can we learn from the cancer and rare disease communities to pull the technology levers and get started, so that doctors will no longer have to experiment on their autoimmune patients? How can we make autoimmune communities less lonely?
If you are working to bring new tools and information to the autoimmune community — whether it is a patient community, a chronic disease management approach or tool, new omics research (genomics, proteomics, microbiome, etc.) or other type of data driven solution – let’s talk so I can include you in my upcoming research.