Childhood Cancer

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Immunoglobulin High-Throughput Sequencing for Refining Risk Stratification in Infant B-ALL

University of Rochester
Carol Fries Simpson, MD
Grant Type: 
Reach Grants
Year Awarded: 
Type of Childhood Cancer: 
Leukemia, Acute Lymphoblastic Leukemia (ALL)
Project Description: 

B-lymphoblastic leukemia (B-ALL) is the most common childhood cancer and has an excellent overall chance of cure. However, infants younger than 12 months old at diagnosis are the exception to this success. Regardless of their more intense treatment, infants with B-ALL are far more likely than older children to experience leukemia relapse. We must improve our ability to predict which infants are destined to relapse so that we can introduce new treatment approaches for those most in need.

One of the most reliable methods for predicting relapse is to test for exceedingly small levels of remaining leukemia early in treatment, known as minimal residual disease (MRD). Children with positive MRD at defined treatment time-points are more likely to experience relapse than children with negative MRD. However, standard methods of MRD detection are particularly unreliable in infants, whose leukemia has unique features making it more difficult to track by conventional methods. Fortunately, our ability to detect MRD has dramatically improved with modern sequencing technology, which takes advantage of unique rearranged sequences of DNA found in leukemia cells. These genetic sequences serve as ‘fingerprints’ for tracking. In addition, the actual rearranged sequences present in a patient’s leukemia at diagnosis may also be useful in helping to predict relapse risk. While these data have been extensively explored and are becoming routine for measuring treatment response in older children, this technology has never been tested in infants with B-ALL.

Project Goal:

Numerous efforts to intensify treatment for infants with B-ALL to improve their chance of cure have failed due to their high relapse rates and particular vulnerability to treatment-related side effects. In addition, there are far fewer existing methods for predicting relapse in infants than in older children, likely resulting in overtreatment for some while others destined for relapse go unidentified until it’s too late. The purpose of this study is to apply an existing and well-established technology for measuring early treatment response – immunoglobulin high-throughput sequencing – to improve our ability to predict and prevent relapse in infants with B-ALL. The data we gather will serve as a critical first step toward personalizing therapy according to individual relapse risk to improve the chance of cure and avoid unnecessary treatment-related side effects for infants with B-ALL.