Replication Profiling as a Diagnostic Tool in B-cell Acute Lymphoblastic Leukemia

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DESCRIPTION (provided by applicant): Despite successes in identifying distinct subtypes of acute lymphocytic leukemia (ALL) with high or low risk of treatment failure, approximately one third of all diagnoses lack any prognostic features and 10-25% of these patients recur. Hence, there is a strong need for new diagnostic tools to improve risk stratification for these patients. We propose a first in class evaluation of replication timing-the temporal order of replication of megabase-sized chromosomal segments-as a prognostic cancer biomarker. Abnormal replication timing has been anecdotally associated with many cancers, but no systematic evaluation of its potential to serve as a source of cancer biomarkers has been performed. Our long-term goal is to evaluate the potential of replication timing to be exploited for therapy as wel as to improve clinical outcome. Our immediate goal is to link unique features of the replication-timing program to outcome for those ALL patients that lack strong prognostic features. Our central hypothesis is that replication-timing differences can distinguish between good vs. poor outcome within this subset of patients. Our preliminary data demonstrate that we can effectively analyze replication timing genome-wide in banked frozen patient samples, and that significant differences ("fingerprints") in replication timing can be identified between ALLs of different orign. The next critical step is to determine whether these differences can be informative for risk stratification. Our rationale is that the most effective way to reach this goal is to analyze a defined cohort of ALL patients that lack strong prognostic features to ask whether replication timing can identify patients that will suffer a recurrence. Aim1 will collect genome-wide replication timing data from 60 banked NCI high-risk B-cell ALL samples with known outcomes that lack strong prognostic features. Aim2 will derive replication-timing fingerprints from the results of Aim 1 and perform statistical analyses correlating these fingerprints with clinical data for the patients. The proposal is significant because, if successful, this project could dramaticaly improve the ability to identify patients at risk of relapse and introduce an entirely novel genre o biomarkers. The approach is innovative because it represents a first in class evaluation of replication timing as a prognostic cancer biomarker. We expect these studies to reveal the power of replication timing fingerprints to associate with patient outcome. As an R21, the "high payoff" is the potential for a whole new genre of biomarkers; the "high risk" aspect is that we have not yet linked replication-timing fingerprints to patient outcome.