Novel Relapse Markers Identified in Pediatric ALL Interview with:
Jason Saliba PhD

Perlmutter Cancer Center
New York University Langone Medical Center
New York, NY What is the background for this study? What are the main findings?

Response: The outcome for children with acute lymphoblastic leukemia (ALL) has improved dramatically over the last four decades, but the prognosis for those who relapse remains dismal, especially for those who relapse while on therapy. In fact, relapsed disease remains a leading cause of cancer related mortality in children. To date, various studies have discovered a number of somatic alterations that contribute to driving relapse and have provided profound insight into the selective forces that lead to clonal outgrowth of drug resistant populations. However, the timing of the initial emergence of the driving mutations along with the speed of clonal outgrowth is unknown.

Whole exome sequencing (WES) was run on available diagnosis, germline (remission), and relapse samples collected from thirteen pediatric ALL patients treated according to Nordic NOPHO ALL protocols. Analyses were then performed to find somatic missense mutations enriched in the relapse samples versus their patient matched diagnosis and/or germline samples. Candidate relapse driving missense mutations were identified as present at high levels (>20%) in the relapse sample, but were undetectable in germline or low to absent in the diagnosis sample. Eight of the thirteen patients contained mutations in genes previously reported to be enriched at relapse. Interestingly, a majority of the patients contained novel candidate relapse specific genes involved in a wide array of cellular processes such as cell adhesion/migration, RNA polymerase II/transcription, circadian rhythm, the unfolded protein response, RNA transport, epigenetic regulation, DNA methylation, and kinases. What are the main findings?

Response: Elucidating these patients’ mutations through exome sequencing allowed us to unlock the unique power of the NOPHO ALL cohort, as peripheral blood samples were drawn from each patient at different time points throughout maintenance therapy. Knowing the major mutations present at relapse, we designed digital droplet PCR (ddPCR) primers and probes that targeted and detected the candidate relapse driving mutations. We then used these probes to detect the patient specific candidate relapse driving clones in the corresponding relapse, remission, diagnosis, and peripheral blood maintenance samples.

Most interestingly, we detected the presence of relapse driving candidates involved in nucleoside synthesis pathways in three separate patients who experienced early relapse in their maintenance sample drawn closest to relapse. In one case, we detected a mutation in NT5C2 at 37% at relapse and .3% in a maintenance sample collected 41 days before relapse. In another patient, a mutation in NT5C2 at 42% at relapse and was detected at .008% in the two maintenance samples collected 116 days prior to relapse. Finally, a mutation in PRPS1 was detected at relapse at 48% and found at .005% in a maintenance sample collected 58 days prior to relapse. All three of these mutations were not seen in any earlier collected maintenance, remission, or diagnosis samples.

Through ddPCR, we were able to detect mutations in maintenance samples down to a sensitivity of .005%. Relapse mutations were able to be detected from 41 to 116 days prior to frank relapse. Once the presence of the mutation is detected, clonal outgrowth to relapse is quite rapid, within 1 – 3 months. What should readers take away from your report?

Response: The main takeaway from our report is the clonal outgrowth of patients who relapse early while on maintenance therapy is rapid. The time between initial detection of the candidate relapse driving mutations and relapse is very short about 1-3 months.

We were able to detect mutations down to a level of .005% in peripheral blood maintenance samples. For the mutations called in the latest collected maintenance samples and at relapse, we were unable to detect these mutations in any earlier maintenance samples or at diagnosis. This indicates that either these particular mutations were acquired by the clone during maintenance therapy or the ddPCR method used was not sensitive enough to detect the mutations in any earlier sample. Improvements in primer/probe design and the development of more sensitive detection methods are necessary. What recommendations do you have for future research as a result of this study?

Response: Our clonal tracking in maintenance samples clearly demonstrates the rapidity of clonal outgrowth that results in frank relapse. More thorough clonal analyses are needed to determine the percentage of relapse driving mutations present at diagnosis versus those that are acquired during therapy. Diligent patient monitoring and blood collection every few months during maintenance therapy may prove effective in reducing the rise of relapse. Technological improvements related to increased sensitivity for real-time mutation detection methods will also be imperative in defending against relapse. Improved detection will allow for the prompt implementation of alternative therapies.

A higher number of samples need to be investigated to determine a potential link between the specific type of therapy being used and the mutations emerging that confer resistance. Additionally, more functional experimental methods need to be deployed to understand the mechanism by which these mutations drive relapse, which could lead to the development of inhibitors and drugs that could counteract the relapse drivers. Ultimately, through the development of more sensitive detection methods, vigilance, and the design of novel inhibitors to quench relapse drivers, the impact and risk of pediatric ALL relapse will be greatly reduced.


Using Whole Exome Sequencing in Pediatric Acute Lymphoblastic Leukemia Germline, Diagnosis, and Relapse Trios to Discover Novel Relapse Enriched Mutations for Clonal Backtracking By Ddpcr
Jason Saliba, Nikki Ann Evensen, Julia Meyer, Igor Dolgalev, Daniel Newman, Ashfiyah Chowdhury, Jacob Nersting, Jinhua Wang, Kjeld Schmiegelow and William L.Carroll
Blood 2016 128:4085;

Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

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Last Updated on December 11, 2016 by Marie Benz MD FAAD