MJS, C-HP, MHC, and WEE designed the experiments and the study. MJS, DP, WY, GS, CC, JCP, MVR, WEE, and MHC analyzed the data. C-HP enrolled and treated patients. NP wrote the first draft of the paper. MJS, WY, LK, GS, CC, C-HP, MVR, MHC, and WEE contributed to writing the paper. MHC collected gene expression and patient data for this study.
The authors have declared that no competing interests exist.
Childhood acute lymphoblastic leukemia (ALL) is the most common cancer in children, and can now be cured in approximately 80% of patients. Nevertheless, drug resistance is the major cause of treatment failure in children with ALL. The drug methotrexate (MTX), which is widely used to treat many human cancers, is used in essentially all treatment protocols worldwide for newly diagnosed ALL. Although MTX has been extensively studied for many years, relatively little is known about mechanisms of de novo resistance in primary cancer cells, including leukemia cells. This lack of knowledge is due in part to the fact that existing in vitro methods are not sufficiently reliable to permit assessment of MTX resistance in primary ALL cells. Therefore, we measured the in vivo antileukemic effects of MTX and identified genes whose expression differed significantly in patients with a good versus poor response to MTX.
We utilized measures of decreased circulating leukemia cells of 293 newly diagnosed children after initial “up-front” in vivo MTX treatment (1 g/m2) to elucidate interpatient differences in the antileukemic effects of MTX. To identify genomic determinants of these effects, we performed a genome-wide assessment of gene expression in primary ALL cells from 161 of these newly diagnosed children (1–18 y). We identified 48 genes and two cDNA clones whose expression was significantly related to the reduction of circulating leukemia cells after initial in vivo treatment with MTX. This finding was validated in an independent cohort of children with ALL. Furthermore, this measure of initial MTX in vivo response and the associated gene expression pattern were predictive of long-term disease-free survival (
Together, these data provide new insights into the genomic basis of MTX resistance and interpatient differences in MTX response, pointing to new strategies to overcome MTX resistance in childhood ALL.
William Evans and colleagues investigate the genomic determinants of methotrexate resistance and interpatient differences in methotrexate response in patients newly diagnosed with childhood acute lymphoblastic leukemia.
Every year about 10,000 children develop cancer in the US. Acute lymphoblastic leukemia (ALL), a rapidly progressing blood cancer, accounts for a quarter of these childhood cancers. Normally, cells in the bone marrow (the spongy material inside bones) develop into lymphocytes (white blood cells that fight infections), red blood cells (which carry oxygen round the body), platelets (which prevent excessive bleeding), and granulocytes (another type of white blood cell). However, in ALL, genetic changes in immature lymphocytes (lymphoblasts) mean that these cells divide uncontrollably and fail to mature. Eventually, the bone marrow fills up with these abnormal cells and can no longer make healthy blood cells. As a result, children with ALL cannot fight infections. They also bruise and bleed easily and, because they do not have enough red blood cells, they often complain of tiredness and weakness. With modern chemotherapy protocols (combinations of drugs that kill the fast-dividing cancer cells but leave the normal, nondividing cells in the body largely unscathed), more than 80% of children with ALL live for at least 5 years.
Although this survival rate is good, some patients still die because their cancer cells are resistant to one or more chemotherapy drugs. For some drugs, the genetic characteristics of the ALL cells that make them resistant are known. Unfortunately, little is known about why some ALL cells are resistant to methotrexate, a component of most treatment protocols for newly diagnosed ALL. Methotrexate kills dividing cells by interfering with DNA synthesis and repair. Cancer cells can be resistant to methotrexate for many reasons—they may have acquired genetic changes that stop the drug from entering them, for example. These resistance mechanisms need to be understood better before new strategies can be developed for the treatment of methotrexate-resistant ALL. In this study, the researchers have determined the response of newly diagnosed patients to methotrexate and have investigated the gene expression patterns in ALL cells that correlate with good and bad responses to methotrexate.
The researchers measured the reduction in circulating leukemia cells that followed the first treatment with methotrexate of nearly 300 patients with newly diagnosed ALL. They also used “microarray” analysis to investigate the gene expression patterns in lymphoblast samples taken from the bone marrow of 161 patients before treatment. They found that the expression of 50 genes was significantly related to the reduction in circulating leukemia cells after methotrexate treatment (a result confirmed in an independent group of patients). Of these genes, the expression of 29 was higher in patients who responded poorly to methotrexate than in patients who responded well. A “global analysis test,” which examined the gene expression profile of different cellular pathways in relation to the methotrexate response, found a significant association between the nucleotide biosynthesis pathway (which is needed for DNA synthesis and cellular proliferation) and the methotrexate response. Finally, patients with the best methotrexate response and the 50-gene expression profile indicative of a good response were more likely to be alive after 5 years than patients with the worst methotrexate response and the poor-response gene expression profile.
These findings provide important new insights into the genetic basis of methotrexate resistance in newly diagnosed childhood ALL and begin to explain why some patients fail to respond to this drug. They also show that the reduction in circulating leukemic cells shortly after the first methotrexate dose and a specific gene expression profile both predict the long-term survival of patients. These findings also suggest new ways to modulate sensitivity to methotrexate. Down-regulation of the expression of the genes that are expressed more highly in poor responders than in good responders might improve patient responses to methotrexate. Alternatively, it might be possible to find ways to increase the expression of the genes that are underexpressed in methotrexate poor responders and so improve the outlook for at least some of the children with ALL who fail to respond to current chemotherapy protocols.
Please access these Web sites via the online version of this summary at
• The US National Cancer Institute provides a fact sheet for patients and caregivers about
• The UK charity Cancerbackup provides information for patients and caregivers on
• The US Leukemia and Lymphoma Society also provides information for patients and caregivers about
• The
• MedlinePlus provides additional information about
Childhood acute lymphoblastic leukemia (ALL), the most common cancer in children, can now be cured in approximately 80% of patients [
The pharmacokinetics and pharmacodynamics of MTX in ALL cells are well understood, whereas the genomic determinants of the antileukemic effects of MTX remain to be elucidated [
A more complete understanding of the mechanisms of MTX resistance in ALL cells is needed if new treatment strategies are to be developed for patients whose leukemia is resistant to this important component of ALL chemotherapy [
A total of 293 children aged 18 y or younger with newly diagnosed ALL, enrolled on the St. Jude Total Therapy XIII and XV protocols, were included in this study (
The flow chart includes study relevant protocol information for the St. Jude Children's Research Hospital Total Therapy Protocols XIIIA, XIIIB, and XV. Specifically, from the population that received ALL treatment according to one of these three protocols, the current study included only patients who received HDMTX as initial therapy. These protocols included a randomization to determine whether patients received HDMTX or not as initial treatment, the infusion time of HDMTX, and whether MP was given after MTX (LDMTX, low-dose methotrexate). Patients with an insufficient number of ALL cells for gene expression analysis were excluded, as were patients with insufficient data on circulating ALL cells to assess response over 3 d.
After stratification for age, WBCPRE, immunophenotype, and sex, patients were randomized to receive initial intravenous treatments of high-dose MTX (HDMTX: 1 g/m2) either as HDMTX4H (HDMTX by infusion over 4 h;
Circulating leukemia cells were measured before therapy (WBCPRE) and at day 3 following start of HDMTX treatment (WBCDay3), prior to the administration of other antileukemic agents. Leukocyte counts were determined with a Coulter counter (model F_STKR; Coulter, Hialeah, Florida, United States).
ALL blasts were obtained from bone marrow aspirates at diagnosis and 42–44 h following treatment. Samples consisted of 5–10 ml of bone marrow collected in syringes containing 800 units of heparin and kept on ice until processed. Leukemic cells were obtained by density separation over a Ficoll-Hypaque gradient and washed three times with a solution of HEPES, Hanks buffered solution, and heparin, as previously described in detail [
Of the 293 patients treated with up-front HDMTX, 161 had sufficient diagnostic ALL cells for gene expression analysis (i.e., had sufficient leukemia cells in their diagnostic bone marrow aspirates to permit RNA isolation from 5 × 106 to 1 × 107 ALL cells). High-quality total RNA was extracted with TriReagent (MRC, Cincinnati, Ohio, United States) from cryopreserved mononuclear cell suspensions from bone marrows at diagnosis. Total RNA was processed and hybridized to the HG-U133A oligonucleotide microarray (Affymetrix, Santa Clara, California, United States). This array contains 22,215 gene probe sets, representing approximately 12,357 human genes, plus approximately 3,820 expressed sequence tag clones with unknown function [
Additional information on the microarray methods and results can be found at
Intracellular MTXPGs were extracted from 42- to 44-h post-treatment bone marrow ALL cells kept in a buffered solution (Tris, EDTA, and 2-mercaptoethanol [pH 7.8]) by first boiling (100 °C for 10 min), then stored frozen at −80 °C until analysis. The HPLC separation and the radioenzymatic quantitation of MTX and six polyglutamylated metabolites (MTXPG2–7) were performed as previously described [
MTX responses as measured by WBCDay3, WBCPRE, and MTXPG values were logarithmically transformed to normalize their respective distributions. The Pearson correlation test was applied in order to determine the association between WBCDay3 and ALL subtype, MTXPG, and WBCPRE. The difference between WBCPRE and WBCDay3, WBCΔDay3 (the WBC residual based on the linear regression of log[WBCPRE] change to log[WBCDay3] ) was determined by taking the residuals of the linear regression model of WBCDay3 versus WBCPRE, which was available for 293 patients. Specifically, MTX response is defined as:
We indicated “MTX poor response” and “MTX good response” in
This plot illustrates the leukemia cell count on day 3 (WBCDay3) after initial HDMTX treatment versus the pretreatment leukemia cell count (WBCPRE) at diagnosis in 293 patients. The solid line indicates the linear regression, and the dotted line the 95% confidence interval with
Hierarchical clustering using the top 50 most discriminant gene probe sets (
Data were available for 161 patients on both WBCΔDay3 and gene expression in diagnostic bone marrow leukemia cells (
For each patient, we computed a gene expression profile using the weighted average of the expression signals of top selected genes. The Pearson correlation coefficient between each gene's expression and WBCΔDay3 was used as the weight. This weighted average of expression signals was used as the summary of the top gene expression profile for each patient. Specifically, the gene expression profile was computed according to the following formula:
Where
We compared the top 50 gene profile with the top 100 gene profile; the correlation coefficient was 0.989 (
We tested 37 GenMAPP pathways and 319 Gene Ontology–biological process (GO–BP) gene groupings for association with WBCΔDay3 using the “globaltest” method [
The percentage of ALL cells in S-phase was determined in diagnostic bone marrow aspirates from patients for whom an adequate number of cells were available (
The duration of disease-free survival (DFS) was defined as the time from diagnosis until the date of leukemia relapse (event), or the last follow-up (censored). Second malignancies and death due to other reasons were censored at the time of occurrence. Treatment outcome was available in 136 patients of the 293 patients treated with HDMTX with WBCPRE and WBCDay3 measured. Of note, patients treated on protocol T15 were excluded because of short follow-up. Time was also censored at the last follow-up date if no failure was observed. Single-variable analysis using Cox proportional hazards regression, as modified by Fine and Gray [
Patient characteristics (race, sex, age, WBCPRE, ALL subtype) were similar among patients randomly assigned to receive HDMTX4H, HDMTX24H, or HDMTX24H+MP (
Patient Characteristics Were Not Different among the HDMTX Treatment Groups
WBCDay3 was significantly lower than WBCPRE (
There is a significant correlation of WBCΔDay3 with the total MTXPG level in ALL cells from 230 patients (i.e., a higher total MTXPG concentration is associated with a better in vivo MTX response) (
Our analyses of antileukemic effects after in vivo MTX treatment and gene expression in pretreatment ALL cells identified the 50 most significant gene probe sets that were associated with antileukemic effect of MTX (WBCΔDay3,
To gain more insight into the molecular and cellular pathways related to MTX response, the global test analysis was used to determine whether the gene expression profile of different pathways retrieved from the GO–BP or GenMAPP database, were significantly associated with the antileukemic effect of MTX. As listed in
We were able to determine both the percentage of cells in S-phase of the cell cycle and gene expression in 154 patients (these are by ALL subtype: B-lineage hyperdiploid,
Pearson Correlation of Selected Biological and Response Parameters with Percentage of Cells in S-Phase
The median follow-up of patients for this analysis was 9.1 y from diagnosis, comprising patients enrolled in St. Jude Total Therapy XIII protocol (
Univariable Hazard Analysis of the Risk of Relapse with Variables Related to Initial In Vivo MTX Response and Multivariable Cox Proportional Hazard Analyses Each Including Known Prognostic Factors (i.e., ALL Subtype, Age at Diagnosis, Risk Group)
Patients with the best MTX response (i.e., bottom quartile of the residual WBCΔDay3) had significantly better 5-y DFS compared to patients with the worst response (top quartile) (DFS ± SE 96.9% ± 3.1% versus 81.4% ± 7%,
(A) MTX response is categorized by WBCΔDay3 MTX good responders (i.e., bottom quartile,
(B) Top 50 gene expression profile is categorized by top 25% (gene profile for good responder,
(C) Proliferation index is categorized by
Furthermore, multivariable Cox regression analysis (
In an independent test set of 18 additional patients who received initial HDMTX according to the St. Jude Total Therapy XV protocol, we performed gene expression analysis at diagnosis and determined WBC (ALL cell) count at diagnosis and on day 3. The gene expression profile of the top 50 genes was significantly related to the residual WBCΔDay3 in this patient cohort (top 50 gene profile,
Relation between the in vivo MTX response (WBCΔDay3) and top 50 gene expression profile (
Additionally, we predicted the WBCDay3 after initial MTX treatment based on the known WBCPRE in these 18 newly enrolled patients. For that, we used either the WBCΔDay3 linear regression function or the median WBC%drop developed in the original test cohort of 293 patients. The sum of the differences between the observed and the predicted WBCDay3 squared was 1.042 using the WBCΔDay3 linear regression model and 3.35 using the median WBC%drop. The observed WBCDay3 values are significantly closer to the predicted values using WBCΔDay3 (
The current studies have identified genes that are expressed at a significantly different level in acute lymphoblastic leukemia cells of patients who exhibit a poor in vivo response to HDMTX. High-throughput genomic approaches to assess the expression levels of RNA transcripts in cancer cells are providing new insights into pathogenesis, classification, diagnosis, stratification, and prognosis of many human cancers [
We therefore evaluated MTX response in vivo after initial therapy, because this is the only possible time to assess the antileukemic effects of MTX as a single agent in patients and because there are no reliable in vitro methods. Thus, our study focused on treatment-naive ALL, and assessed de novo resistance. This revealed that WBCΔDay3 is a superior measure of in vivo MTX response when compared to the percentage drop in leukemia cells (i.e., WBC%drop), and that WBCΔDay3 was predictive of long-term DFS. Furthermore, the difference in survival cannot simply be explained by differences in MTX systemic exposure (
To better understand the biological basis underlying MTX response in ALL cells, we used an unbiased genome-wide approach to identify genes whose expression in primary leukemia cells in vivo was significantly related to WBCΔDay3. This process revealed 48 genes and two cDNA clones that are highly related to the in vivo MTX response (WBCΔDay3), even after adjusting for MTXPG accumulation (
Our analysis also showed that low expression of
Our current data showed that low cell proliferation levels, in addition to our measure of in vivo MTX response, is an important ALL cell characteristic related to worse outcome. This result is in agreement with those of a previous study that found treatment-naïve blasts with a low proliferation rate are more resistant to several anticancer drugs in vitro [
Interestingly, other known folate metabolism genes were not among the top genes, suggesting that expression of the known folate metabolism genes in pretreatment ALL cells is less important in causing de novo MTX resistance than previously thought. It may well be that these folate pathway genes are important for the acquired drug resistance that emerges during MTX treatment. It is also plausible that expression or function of these proteins is not reflected by the level of their mRNA expression in ALL cells. These possibilities merit further investigation, which is beyond the scope of the current work.
Defining the genomic determinants of ALL resistance to individual antileukemic agents is essential if the pharmacogenomics of drug resistance are to be elucidated, because the current and prior studies have shown that genes discriminating drug resistance in ALL are drug specific [
Among the 50 genes that were expressed at a significantly different level in leukemia cells of MTX good responders versus poor responders, 29 were overexpressed in the MTX poor- responders. It is plausible that these overexpressed genes would be candidate targets for small molecules or other strategies to down-regulate their function, as a means to modify MTX response. Such a strategy has already proven successful in finding agents to modify the sensitivity of ALL cells to steroids [
The current study is the first, to our knowledge, to identify genes whose expression is related to in vivo MTX response in patients with newly diagnosed ALL. Our data provide new insights into the genomic basis of interpatient differences in MTX response and point to new strategies for overcoming de novo MTX resistance in childhood ALL. In addition, our data indicate that early treatment response to MTX is a significant prognostic indicator for long term DFS in children with ALL.
Gray boxes indicate data used for the analyses, white boxes intermediate data, shaded boxes data analysis method used, and
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The three initial treatment groups with HDMTX were not different in their WBCDay3 (HDMTX24H,
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Shown are the distributions of 293 patients for (A) WBCLogChange that is defined as log(WBCPRE) minus log(WBCDay3),
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The gene plot gives a bar and a reference line for each gene probe set categorized for this pathway. The bar indicates the influence of each probe set on the correlation with MTX response (WBCΔDay3). If the height of the bar exceeds the reference line the probe set is significantly related to MTX response. Marks indicate the standard deviations by which the bar exceeds the reference line. Red indicates gene probe sets with a positive correlation and green indicates gene probe sets with a negative correlation with MTX response.
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Shown are the scatterplots for 154 patients correlating percentage of leukemia cells in S-phase with (A) the expression of
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There was no difference (
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The column titled “probe-set correlations with WBCΔDay3” indicates whether most probe sets in the pathway have a positive correlation, a negative correlation or a mixture of positive and negative correlations with WBCΔDay3.
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Individual factors related to MTX response are highlighted in gray (i.e., WBCΔDay3, TYMS, DHFR, top 50 gene profile), each including known prognostic factors (i.e., ALL subtype, age at diagnosis, risk group).
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MIAME-compliant primary microarray data are available through the Gene Expression Omnibus (NCBI) at
We thank the patients and their parents for their participation in this study; our clinical staff for facilitating protocol-based patient care; and our research nurses, Sheri Ring, Lisa Walters, Terri Kuehner, Margaret Edwards, Vickey Simmons, and Paula Condy. We also thank Yaqin Chu, May Chung, Emily Melton, and Siamac Salehy for outstanding technical assistance; and Nancy Kornegay and Mark Wilkinson for computer and database expertise.
ATP-binding cassette
childhood acute lymphoblastic leukemia
disease-free survival
false discovery rate
Gene Ontology–biological process
high-dose methotrexate
hazard ratio
methotrexate
methotrexate polyglutamate
percent WBC change
white blood cell count
MTX response as defined by the WBC residual based on the linear regression of log(WBCPRE) change to log(WBCDay3)
peripheral WBC count on day 3 following start of HDMTX treatment
peripheral WBC count pretreatment