From targeted therapy to precision medicine

Harrison continued a low dose of chemo until 2015 and remained in remission until 2016, when cancer returned to his central nervous system. This time, targeted therapies had gained even more traction in the health care industry, and doctors found another new treatment protocol, CAR T-cell therapy.

“CAR T therapy empowers the immune system to recognize and kill cancer cells,” says McKinion. Your own white cells attack cancer cells the same way they would attack a bacteria.”

Today, thanks to an amazing combination of factors – including persistence, targeted therapies and immunotherapy – Harrison is an active, cancer-free teenager.   

“At every stage, Harrison was just months beyond a cutoff date,” says McKinion. If he had presented with cancer earlier in either case, the treatment that saved his life would not have been available."

McKinion, who has a doctorate in divinity, has become an advocate for families affected by childhood cancer. He testified before Congress in 2016 to recommend more funding for childhood cancer research, and he is co-founder of Harrison’s House, a nonprofit in Wake Forest, NC, that provides camp experiences for teens with cancer, serious illnesses and special needs.

“If it wasn’t for data and the use of big data in research, Harrison would have been dead seven years ago," says McKinion. "Now he’s about to graduate from high school. He’s playing baseball and soccer, and he wants to go change the world.”

What career plans does Harrison have after college? “He wants to study data and analytics,” says McKinion. “His goal is to change the world through data because he knows what it’s meant to him and his life.”

As cancer research and cancer treatments evolve in this new world of precision medicine, analytics plays a crucial role. From treatment innovation to clinical research, and from medical imaging to early efficacy tests – the importance of analytics in cancer care and diagnosis cannot be overlooked. 

“Analytics not only helps discover new therapies, but also measures their impact once they are in use, and it helps to financially reward the therapies that deliver the highest patient value,” says Lambrecht. 

The type of therapy that eventually put Harrison in remission when his cancer relapsed, CAR T-cell therapy, works by enhancing a patient’s T-cells to fight off the most resistant cancer cells. The treatment uses a combination of cellular therapy, gene therapy and immunotherapy to boost a patient’s immune system to become an advanced cancer-fighting machine.

Currently, CAR T-cell therapy is more effective in treating blood cancers like leukemia and lymphoma. However, the development of CAR T-cells for nonblood, or “solid,” cancers has been more difficult.

Researchers at Oslo Cancer Cluster have discovered a new way to model solid cancer cells in order to test CAR T-cells on those models

The ultimate goal of the consortium is to uncover the best treatment for each unique patient. Analytics is a crucial part of that effort.

“In cancer treatment right now, there is a digitization of oncology,” explains Ketil Widerberg, General Manager of Oslo Cancer Cluster. “Analytics can achieve two things for cancer research. One is to understand cancer better, to be able to see patterns we didn’t see before. Secondly, we use analytics to understand how we can treat patients better, to give the right treatment to the right patient at the right time.” 

Data sharing offers hidden insights

While immunotherapy focuses on medicine at the personal level, many efforts to advance cancer care require data from a lot of different patients.

Clinical trials, including those for immunotherapy treatments, are intentionally and meticulously designed to study the specific effects of a specific therapy on a specific type of patient. This level of control is needed to ensure the safety and efficacy of the treatments being studied.

But researchers today are realizing that the answers to many complex questions lie outside of those controlled study environments – and one solution is to open clinical trial data for analysis across multiple clinical trials. Consider these three programs that combine data for advanced research: 

1. The Project Data Sphere cancer research platform sparks innovation by opening data to new research possibilities. To support these efforts, SAS hosts the research platform and provides access to analytics technology at no cost to researchers. The data in the platform is deidentified and consistent with industry requirements.
2. BioGrid Australia, a nonprofit organization, combines data from hospitals and research institutions to improve clinical research into various cancers, like bowel cancer. The project brings together data from patient screenings at 19 sites across the country.
   
3. The Latin American Cooperative Oncology Group (LACOG) explores data from thousands of cancer patients at more than 150 hospitals and 15 countries throughout the region. The project is helping identify known roadblocks to optimal care, such as poor access to treatment, medication and preventive care. The long-term goals are to better serve patients, facilitate the development of new techniques and technologies to improve cancer care, and even affect public policy.

Greater access to patient-level clinical data is good for patients, science and business. From a business perspective, pooling data can significantly reduce the cost of drug development while improving the efficiency of clinical trials. 

“A large number of kids with immunotherapy respond immediately. But some do not. You have to figure out for some kids why it doesn’t take. The possible answers are so vast, there’s no way to do it without big data,” says McKinion.

Ultimately, the sooner doctors can identify what is or isn’t working, the sooner they can put patients on a path to a treatment that works for them.

At Amsterdam University Medical Center, a team of radiologists and surgeons recently began using computer vision, a form of artificial intelligence, to improve the process of identifying cancer tumors and measuring the changes in tumors after treatment. Their initial project uses object detection to identify and measure tumors in CT scans of livers from patients with colorectal cancer that spread to the liver. 

Typically, radiologists measure the size of tumors manually in the scans before and after treatment. This is very time consuming and is prone to subjectivity, but it is crucial work. If the patient’s tumors are responding to treatment, that also makes the patient a good candidate for surgery.  

Leaders at the hospital saw this as a perfect pilot project to test the capabilities of analytics and AI. Computer vision models are constructed to analyze the medical images in a fraction of the time, and object detection is used to recognize tumors and tumor sizes almost instantaneously. AI models are more objective and accurate than the radiologist’s measurements alone. 

This use of AI not only frees up radiologists to do more hands-on work with patients, but also improves decisions and therefore could potentially save lives. By finding results faster and more accurately, computer vision can improve treatment strategies with the potential to lead to better outcomes of cancer patients. 

Lambrecht predicts many other uses for AI in the fight against cancer, including the ability to generate biomarkers with deep learning that can better determine endpoints for treatment based on safety and efficacy. 

Of course, umbrella studies and other newer research formats for precision medicine generate more data and more complex data than standard trials. But today’s advances in analytics – including new machine learning algorithms and faster processing speeds – are up to the task.

Cancer patients and their families, like the McKinions, are grateful.