Why me? That was the question Jay “Marty” Tenenbaum couldn’t answer, the one that haunted him.
He did not ask himself this question in 1998, when he was first diagnosed with stage 4 metastatic melanoma and told he needed to get his affairs in order. He was an electrical engineer by schooling and a pioneering computer scientist by trade. He understood how to process data and probabilities better than almost anyone. At that low moment, he understood the why — he had a cancerous mutation, the sort that kills millions every year.
What he did not understand was what happened next. After undergoing experimental treatment in a failed drug trial, Tenenbaum was an “exceptional responder” — one of a tiny few for whom the drug worked, causing the cancer to go into remission.
Tenenbaum could have simply considered himself lucky and carried on with his successful business career. But his outlier result made no sense to him. Why him? Why was he alive? If all metastatic cancer is the same, he thought, I would be dead. The only reason I’m not dead is every cancer must be unique.
This became an obsession for Tenenbaum. He began to re-evaluate the entire standard-of-care treatment model that dominated healthcare, asking why there was not a more personal approach to cancer treatment. The harder he looked, the more frustrated he became. With few exceptions, he found silos within the patient-treatment ecosystem — oncology, research, care — that shared little to no data, that prolonged treatment getting to market, that were, in effect, operating with blinders.
Healthcare systems need to think about how they can educate patients to become co-researchers in this fight.
An innovative computer scientist who had revolutionized the e-commerce market, Tenenbaum knew a key to fixing this problem was compiling a better database. To make a breakthrough in cancer care, he believed that the more patient data he could gather — everything from genomic data to medical records to diaries of patient experiences — the better chance there was to find revelations in data where most only see confusion and contradiction.
“The patients held the answer,” Tenenbaum says. “The patient was being ignored, and they were front and center who we needed to serve.”
Thus, in 2011, Tenenbaum founded Cancer Commons, a nonprofit that takes information from patients and combines it with data from researchers so that members can be guided to the best treatment for them. Cancer Commons gathers data from a patient’s diagnosis, treatment history, results, side effects — any relevant experience. The anonymous patient data is then reviewed by experts and compared to what Cancer Commons has learned from its growing database to match each person to the best treatment.
For example, if you have lung cancer, your profile review might lead Cancer Commons experts to recommend you talk to your care team about appropriate molecular biomarker tests, such as a Pervenio test for stage 1 or 2 patients who might need surgery. They could point you to clinical trials of which your team might not be aware, suggest appropriate targeted therapies, or give you information on new research that could affect your treatment decisions. Throughout the process, they would also recommend second and third opinions and other specialists. Right now, Cancer Commons is focused on lung cancer, prostate cancer, and melanoma, but as the community grows, it hopes to expand its mission to include other types of cancer.
It’s the very model of personalized medicine, a way for cancer patients to have their unique challenges analyzed against the largest data set possible and be shown the most relevant data to their individual case. This is important for patients who often feel like they are adrift once they’re diagnosed, Tenenbaum says.
But Cancer Commons’ effect goes beyond helping individual members. It offers a blueprint for a larger industry disruption, one that places a premium on data sharing to improve individual diagnosis and outcomes.
To make the database large enough where this can occur, Cancer Commons has spent the first three years developing channels to connect with cancer patients. Most obvious is partnering with advocacy groups. They have a ready-made community of engaged patients both in person and online. Cancer Commons offers those groups the ability to collect, store, and leverage their community experiences and also allows them to compare and share that data with other groups, greatly accelerating the learning possibilities from these experiences.
This crowdsourcing approach is essentially how the Internet started: taking regional networks of information and collaboration, then connecting them to each other. Trying to build a cancer database using this model is why one of Tenenbaum’s most important hires was Executive Director William Wong, a Stanford classmate with whom Tenenbaum had first explored the idea of getting smarter using collaborative research from engineers across the country. (They were creating software that could help engineers design new computer chip technologies.) “Even then , Marty spent a lot of time talking about how one could take something being done in research anywhere — prototyping or academic research — and better partner with industry. How you could take that new information and explode it out to the world,” Wong says. “His ideas were clearly prescient.”
Wong went on to spend the next two decades developing things that make doing e-commerce possible. (If you’ve ever paid for an Internet purchase via credit card or deposited a check by taking a picture of it, you at least partly have Wong to thank.) This helps Tenenbaum and Wong as they travel their other major path in which Cancer Commons engages with patients: through healthcare providers (cancer centers, academic centers, etc.).
The goal is obvious: go where the patients are and use their information to create a feedback loop not only for Cancer Commons, but also for the doctors treating those patients. Learn, improve, and optimize service.
It’s not that simple, of course. Tenenbaum and Wong describe longstanding institutional barriers to gathering patient data, ranging from patient privacy to competition for patients to the idea the data has proprietary value.
Tenenbaum says the concerns of many care centers don’t hold water. “People tell me we can’t release our data because people will compare us to a center that doesn’t take higher-risk patients,” he says. “But we can normalize [control] for that.”
Wong concurs. “They want to hang on to their data, even though most institutions aren’t doing anything with it.” As for privacy? “Patients just aren’t that focused on privacy when they’re dying,” Wong says. “There are many ways to mask personal information.”
The hope is that by linking every element of the healthcare system to an ever-increasing patient data set, antiquated standard-of-care approaches like slow-moving large-scale clinical trials will self-correct. They’ll be forced to by the market and patient demands. Then, Wong says, you’ll see the elimination of patient-interaction models that are “20 years out of date. We’ll have patient-relation management systems manage multiple channels — in person, mobile, online — that can figure out how to serve the customer 24-7, learn from the data they gather, and offer better personalized service.”
All of which sounds like aggressive change for a slow-moving industry. But it’s nothing compared to Cancer Commons’ stated goal: to see cancer deaths reduced by 50 percent in 10 years.
“Healthcare systems need to think about how they can educate patients to become co-researchers in this fight,” Wong says. “Then you think about the layers and support around that — social networks, advocacy groups, etc. — it grows out from that. Through their data and their stories, our patients are combining to spread information. We give them a road map to engage. And once they’ve had a taste of the power of this, it doesn’t stop. It just grows from there.”