Determining Treatment Relapse at Diagnosis in Children with ALL
Researchers at the Stanford University School of Medicine have developed a technique that allowed them to determine at diagnosis whether children with acute lymphoblastic leukaemia would relapse following treatment.
The method, described in a paper published in Nature Medicine, predicted relapse in the cohort they examined with 85% accuracy, a significant improvement from the 66% accuracy achieved by the current risk stratification method used at diagnosis.
The method examines cancer cells one at a time using mass cytometry, a technique developed by Garry Nolan, PhD, professor of microbiology and immunology and a senior author of the study. Using data on the cells’ stage of development and signalling behaviour, the scientists figured out how to identify a tiny subset of malignant cells that, if present, predisposed a patient to relapse.
Called the Developmentally Dependent Predictor of Relapse, the technique could help identify which acute lymphoblastic leukaemia patients need a different approach to cancer treatment, and may provide good clues about how to find new drugs to target the deadliest cancer cells, the researchers said.
“We really need to personalise treatment to leukaemia patients better than we do now”, said graduate student Zinaida Good, the study’s co-lead author. “There is a lot of room for improvement here. This study makes a contribution to our ability to stratify patients better and not treat everybody the same way”. Postdoctoral scholar Jolanda Sarno, PhD, is the other lead author.
Pediatric acute lymphoblastic leukaemia is the most common childhood cancer, diagnosed in about 3,000 American children per year. The study focused on the most frequently found type of the disease, called B-cell precursor ALL, which occurs when certain white blood cells take a wrong turn during development and become malignant. Although the majority of cases are cured with existing chemotherapy drugs, 10-20% of patients relapse. Among those who relapse, about 40-80% die of their disease within five years.
“Acute lymphoblastic leukaemia is a very well-characterised cancer that has a robust risk prediction measure already, but the final risk of relapse is usually not known until a few months into treatment, and there are still patients who get missed,” said Kara Davis, DO, assistant professor of pediatric haematology and oncology and the other senior author of the study. “And, with existing prediction tools, when we do identify someone as high-risk for relapse, we don’t know what it is about their leukaemia that raises their risk.”
A Few Really Bad Apples
Prior research strongly suggested that relapse may be driven by a few treatment-resistant cells that are present from the beginning of the disease. “We wondered, can we identify those cells at the time the patient first presents to a clinic, and can we treat patients with a specific therapy to target them?” Davis said.
Using mas cytometry, the researchers tested bone marrow samples taken from 60 ALL patients at the time of their diagnosis. Each patient had 3 to 15 years of follow-up medical records available for analysis, including information on whether they had relapsed.
To identify the problematic cells from among the millions of cells in each sample, the researchers had to figure out how to organise the data. “Every patient has vastly different features to their cancer, and we had to ask, ‘is there any common thread between them?'” Davis said.
The solution, the team found, was to compare leukemic cells to their most similar normal cells along the trajectory of healthy B-cell development. Of 15 developmental cell stages examined, malignant cells arising from just two adjacent stages in B-cell maturation—the pro-B2 and pre-B1 stages—were the bad actors: If these particular types of malignant cells had certain signalling behaviour at diagnosis, patients were almost certain to relapse after standard chemotherapy.
“Stem cell biology is evolving, and we’ve learned a lot about how normal development takes place,” Good said. “Now we can use that to understand cancer better.”
Combining Methods Gets Better Results
When the new method for predicting relapse was combined with existing methods based on patients’ early response to treatment, the results were better than those obtained by either method alone.
“We do not understand the mechanisms by which malignant cells from the pro-B2 and pre-B1 stages of development resist treatment,” Davis said, adding that the team has begun looking for existing drugs to target them.
They plan to validate their method in a larger number of patients and to evaluate whether the same general approach could predict relapse in other forms of cancer. Further, since the method provides information about treatment-resistant cells, patients found to be at high risk for relapse could benefit from treatments specific to those cells.
“We think that being more precise in risk prediction could benefit patients at both low and high risk for relapse,” Davis said.
Materials provided by Stanford University School of Medicine. Note: Content may be edited for style and length.