The goal of our study was to identify gene expression signatures in diagnostic samples that are predictive of early response to therapy and overall outcome in children with National Cancer Institute–defined HR ALL. All samples studied in these experiments were from patients who were treated on a single, contemporary protocol and who received intensified therapy according to a COG-modified Berlin-Frankfurt-Munster backbone, which thus minimized the effects of treatment variables.
Early response to therapy has proven to be one of the strongest predictors of outcome and now is routinely used to stratify patients according to the risk of relapse.17
We were able to identify and validate a gene expression signature that correlated with the kinetics of regression of tumor burden, as assessed by the bone marrow blast content on day 7. Apoptosis-facilitating genes, such as BIM
, were upregulated in RER patients, whereas multiple genes involved in cell adhesion (eg, GPR56, PARVG
), cell proliferation (eg, CKLF, BMP2
), and antiapoptosis (eg, BCL2, SOCS2
) were upregulated in SER patients. If this signature is validated with additional research, more rapid approaches to assessment of gene expression could be used so that augmented therapy might be deployed early—within the first few days of diagnosis—to overcome slow response and possibly the emergence of drug-resistant clones and, ultimately, to improve outcome.
Other investigators also have sought to identify gene expression profiles associated with early response to therapy. Two recent publications from Flotho et al18,19
have portrayed signatures that correlated with minimal residual disease at day 1918
and at day 4619
of induction. Though only five of 44 probe sets from the day 19 signature reached statistical significance in our data set of day 7 response, the trend of association for all the probe sets was remarkably strong. Not surprisingly, this trend was not observed with the day 46 signature (data not shown). Previous studies show that the kinetics of blast reduction is quite steep in the first 2 weeks of induction and is much slower thereafter.20
Thus, although day 7 bone marrow morphology and end induction minimal residual disease may correlate,21
it is likely that fundamental differences exist in the mechanisms of leukemia cell death that occurs in early compared with late induction.
Though various groups have performed microarray experiments on childhood ALL samples, it has proved difficult to identify a prognostic signature at diagnosis. For example, Yeoh et al7
were able to detect distinct expression profiles that predicted relapse in T-cell acute lymphoblastic leukemia and hyperdiploid ALL but not in other subtypes.7
Although expression of OPAL1
predicts ALL in some studies, it has not been validated in others, which suggests that differences in treatment may influence the prognostic impact of expression profiles.22
Other investigators have correlated gene expression signatures with in vitro drug response.14,23
However, this drug resistance profile was not selected for its prognostic value and, hence, may not represent the best selection of outcome-predictive genes. Despite these challenges, we have identified a gene expression signature that was predictive of long-term outcome and was validated in three independent cohorts of diagnostic samples from children who were treated on different protocols, which thus yielded an accurate perspective on the validity and reproducibility of the results.
Almost all of the genes that comprised our predictive signature were not identified in the studies mentioned above that looked at drug resistance and/or outcome. However, studies that have used microarray methodology to discover predictive signatures in other cancers also have shown little overlap in gene lists. Although these gene lists may not always be concordant between data sets, each signature still may be significantly predictive across the data sets. For example, five recently published predictive gene sets for outcome in breast cancer showed little overlap between sets.24
However, four of five were predictive of outcome in a single data set of 295 women, which emphasizes that, despite the lack of overlap, the signatures are reflective of common biologic subsets. This is consistent with our findings that demonstrated the ability of individual gene expression signatures and the derived models by using the COG samples to predict outcome on three different cohorts of patients.
The utilization of predictive signatures in clinical cancer trials is eagerly awaited. The application of array technology to define additional patients with ALL who have a poor outcome may be more difficult given the high cure rate of ALL and the elucidation of many well-established risk factors to date. One of the most crucial findings of our study was that, although gene expression signatures correlated with outcome in univariate analyses in multiple data sets, they lost much significance when well-known outcome predictors, like age, initial WBC, and genotype, were taken into account. A logical interpretation of these findings is that the most important variables associated with treatment failure in ALL have been identified already. However, the inability to accurately predict outcome uniformly by using these conventional variables may be related in many instances to host factors. In addition, measurements of gene expression do not take into account important events, such as post-translational modifications. Another explanation is that prognostic signatures may exist within biologic subtypes of ALL only. It has been established that gene expression profiles correlate with ALL cohorts defined by molecular changes, such as translocations and ploidy. We specifically focused our efforts on National Cancer Institute–defined HR ALL, because known genetic subtypes account for only a minority of patients in this cohort, and we sought to identify novel biologic subtypes associated with outcome by using gene expression profiling. Our inability to define such a group might reflect the existence of smaller biologic subsets within this population that may not be possible to detect with the number of patient cases studied here. However, our study and similar ones by others, even if not predictive in multivariate analysis, are likely to lead to a biologic understanding of why certain clinical and laboratory variables are associated with clinical outcome. Such information is essential to derive more effective, tumor-specific therapies.
In summary, we have identified a gene expression signature that is significantly predictive of outcome in childhood ALL, but it does not seem to provide additional information beyond that contained in already established prognostic variables. The analysis of a larger number of samples may allow investigators to discover gene signatures that provide additional prognostic information. Strict adherence to uniform protocols for sample acquisition, processing, and array experimentation may facilitate comparison between data sets.25
In addition, analysis of gene expression profiles may lead to a biologic understanding of why clinical and laboratory variables are associated with outcome, and this information potentially may be exploited therapeutically.