Once doctors diagnose patients with ALS, it is nearly impossible to predict how long they will live. While the average patient survives about three years, some succumb within months; yet others live for decades. The uncertainty makes clinical trials expensive to conduct because so many patients must participate to outweigh the variability in outcomes. For this reason, Prize4Life proposed its DREAM-Phil Bowen ALS Prediction Prize4Life Challenge (or ALS Prediction Prize) to get people thinking about how scientists might track disease progression. Participants were granted access to three months of data from previous ALS clinical trials and were asked to come up with statistical solutions to predict how rapidly or slowly the patients would decline over the next nine months.

“If you have both rapid progressors and slow progressors in a clinical trial population, it’s harder to tell what a drug is doing,” said Melanie Leitner of Prize4Life. “If you know a priori that patients are on a steep or shallow disease trajectory, scientists can better measure a therapy’s impact.”

Prize4Life originally intended to award one prize of $25,000. Two teams tied for first place and received $20,000 each. Liuxia Wang and Guang Li of Sentrana, a DC-based scientific marketing company, dug up new variables to refine an algorithm that predicts disease progression. They subdivided the 10 component subscores of the ALS Functional Rating Scale (ALSFRS) into five categories related to a body part, such as face, arms, etc. They found that the rapidity or stability of the face-related scores, which include tests of speech and swallowing, best predicted the overall disease trajectory, and that a decline in chest-related scores signaled the endstage of the disease. Lester Mackey of Stanford University and Lilly Fang, a recent graduate there, found that the elapsed time from disease onset best predicted the nine-month trajectory of ALS, and that past rate of decline on the ALSFRS, fluctuations in body weight, and speaking ability were also highly predictive of disease progression. For his runner-up prize of $10,000, Torsten Hothorn, Ludwig-Maximilians-Universität in Munich, Germany, used a statistical method to account for the frequent missing values in the ALS patient data and to predict disease progression based on deterioration on the ALSFRS in the first three months of trial observation.

Altogether, these methods account for about 50 percent of variation in progression seen in ALS clinical trial populations, said Leitner. “Not only will we have a better sense of what is going to happen to an individual patient, but we also potentially reduce the number of patients by 20 to 25 percent” she told Alzforum. This could translate to a savings of $20 million for a $100 million trial.—Gwyneth Dickey Zakaib.