[Cell Cycle 7:10, 1315-1320; 15 May 2008]; ©2008 Landes Bioscience
Extra View
Mechanism of relapse in pediatric acute lymphoblastic leukemia Michelle J. Henderson,1,* Seoyeon Choi,1 Alex H. Beesley,2 Rosemary Sutton,1 Nicola C. Venn,1 Glenn M. Marshall,3 Ursula R. Kees,2 Michelle Haber1 and Murray D. Norris1 1,†Children’s Cancer Institute Australia for Medical Research; Sydney, Australia; 2Telethon Institute for Child Health Research and Centre for Child Health Research; University of Western Australia; Perth, Australia; 3Centre for Children’s Cancer and Blood Disorders; Sydney Children’s Hospital; Randwick, Sydney Australia †Children’s
Cancer Institute Australia for Medical Research is affiliated with the University of New South Wales and Sydney Children’s Hospital; Randwick
UT E
.
Abbreviations: ALL, acute lymphoblastic leukemia; MRD, minimal residual disease; NOD/SCID, non-obese diabetic/severe combined immune deficient strain of mice; PCR, polymerase chain reaction; Ph+, philadelphia chromosome-positive; RQ-PCR, real-time quantitative PCR
OT D
of this disease, relapse following remission induction chemotherapy remains the major barrier to survival, being responsible for death in close to 20% of cases.3,4 Advances in our knowledge of the process of relapse in ALL patients are critical for further improvements in disease outcome for these children. It is generally accepted that bone marrow relapse in ALL, particularly when it occurs within 5 years of diagnosis, results from residual leukemic cells that have survived therapy. The level of minimal residual disease (MRD) at the end of induction chemotherapy is therefore an indicator of disease chemo-resistance. MRD monitoring during the course of treatment has proven to be a powerful tool in predicting relapse in childhood ALL and is currently used to stratify patients to risk-directed therapy.5-10 The level of disease in the patient can be monitored by PCR-based detection of clonal antigen receptor rearrangements in patient bone marrow samples obtained at diagnosis and at various follow up intervals.11,12 Use of this technology has highlighted the occurrence of clonal evolution within the leukemic population between diagnosis and relapse, since new rearrangements may appear at relapse or those detected at diagnosis may be lost. Several recent studies have used these apparently new rearrangements to trace the ‘relapse clone’ back to a minor population present at diagnosis and in some cases relative resistance of this population to remission induction chemotherapy has been observed.13-17 Understanding the basis of this resistance is a major focus of research towards disease eradication.
08
LA
ND
ES
BIO
SC
IEN CE
.D
ON
Relapse following initial chemotherapy remains a barrier to survival in approximately 20% of children suffering from acute lymphoblastic leukemia (ALL). Recently, to investigate the mechanism of relapse, we analysed clonal populations in 27 pairs of matched diagnosis and relapse ALL samples using PCR-based detection of multiple antigen receptor gene rearrangements. These clonal markers revealed the emergence of apparently new populations at relapse in 13 patients. In those cases where the new ‘relapse clone’ could be detected in the diagnosis population, there was a close correlation between length of first remission and quantity of the relapse clone in the diagnosis sample. A shorter length of time to first relapse correlated with a higher quantity of the relapsing clone at diagnosis. This observation, together with demonstrated differential chemosensitivity between sub-clones at diagnosis, indicates that relapse in ALL patients may commonly involve selection of a minor intrinsically resistant sub-clone that is undetectable by routine PCR-based methods. From a clinical perspective, relapse prediction may be improved with strategies to detect minor potentially resistant sub-clones early during treatment, hence allowing intensification of therapy. Together with the availability of relevant in vivo experimental models and powerful technology for detailed analysis of patient specimens, this new information will help shape future experimentation towards targeted therapy for high-risk ALL.
IST RIB
Key words: acute lymphoblastic leukemia, leukemia relapse, minimal residual disease, clonal antigen receptor rearrangements, expression profiling
©
20
Introduction
Acute Lymphoblastic Leukemia (ALL) is the most common cancer in children and is one of the leading causes of childhood death from disease in developed countries.1,2 Despite advances in the treatment *Correspondence to: Michelle J. Henderson; Children’s Cancer Institute Australia for Medical Research; Experimental Therapeutics Program; PO Box 81 (High St); Randwick; Sydney, NSW 2031 Australia; Tel.: +61.2.93820912; Fax: +61.2.93820130; Email:
[email protected] Submitted: 02/21/08; Accepted: 02/29/08 Previously published online as a Cell Cycle E-publication: http://www.landesbioscience.com/journals/cc/article/5885 www.landesbioscience.com
Mechanism of Relapse: Pre-Existing or Acquired Resistance? Two hypotheses have been put forward to account for the drug-resistant nature of the leukemic cells that dominate at relapse (Fig. 1). According to one hypothesis, relapse could arise from induction of resistance during chemotherapy, presumably via acquisition of genetic changes or mutations after diagnosis (Fig. 1A). Such an event would be expected to occur within a single cell of the residual leukemia population and the time to relapse following this change would be determined by the timing of the change and the resulting phenotype. Alternatively, relapse could arise through selection and
Cell Cycle
1315
UT E IST RIB OT D
.D
ON
further expansion of an already relatively resistant sub-population present at diagnosis (Fig. 1B). Several years ago, a study using semiquantitative PCR to measure changes in patients’ leukemic cell burden under therapy reported a biphasic response and suggested that this might indicate differential sensitivities of leukemic populations at diagnosis.18 Indeed, a subsequent study found that in one patient sample, a minor clone present at diagnosis showed relative resistance to therapy and then later re-emerged as the major clone at relapse.15 In a separate study, a p53 mutation detected at relapse in one case of ALL could be traced back to a minor clone present at diagnosis, indicating a probable mechanism of resistance already present in a small proportion of cells at diagnosis.19 Still, it was not clear from these isolated cases whether further changes were commonly acquired during treatment. To investigate this question, we analysed samples of bone marrow taken from children at the time of their diagnosis and compared these with samples taken from the same children at the time of relapse.20 These matched diagnosis and relapse ALL samples were obtained from 27 patients and analysed for clonal populations using PCR-based molecular detection of multiple antigen receptor gene rearrangements. Rearrangements were detected in the diagnosis samples of all 27 patients and in all but one patient, at least one of these clonal markers had re-emerged at relapse. However the clonal marker profiles of 13 patients revealed the emergence of additional clonal populations at relapse. If these ‘relapse clones’ could be traced back to the respective leukemic population at diagnosis, the relapse rearrangement would provide a means to follow the progression of the clone during the course of the disease, which may indicate that expansion of a minor clonal population was responsible for relapse. In order to determine whether the newly identified relapse rearrangements had been present at the time of diagnosis in these 13 patients at low levels, more sensitive leukemia-specific real-time quantitative PCR (RQ-PCR) assays were developed for one or more of the identified relapse markers. This was performed for 12 of the patients (Table 1), while a sensitive and specific assay could not be developed for the thirteenth patient. In eight of these 12 informative patients, a clone-specific relapse marker was identified in the corresponding patient sample taken at the time of diagnosis (Table 1). Thus in eight patients the ‘relapse clone’ had actually been present at very low levels at the time of diagnosis. Moreover, by tracing the levels of the clone through the course of the disease it was possible in one of these patients to demonstrate that the relapse clone was less responsive to remission induction therapy than the major clone present at diagnosis, indicating that it was relatively resistant even at this early time point.20
.
Mechanism of relapse in leukemia
20
08
LA
ND
ES
BIO
SC
IEN CE
Figure 1. Alternative models to explain emergence of therapy-resistant clones at relapse in ALL. Two scenarios are presented (A and B) in which a considerable response to therapy is observed. (A) Mutations arising in cells that survive therapy could provide the material for generation of a resistant population of cells which are free to repopulate the bone marrow and cause relapse. (B) Alternatively, a sub-population of therapy-resistant cells could already exist at the time of diagnosis, yet go undetected due to its low abundance. Being unaffected by therapy, it eventually expands to cause relapse.
©
New Evidence for Selection and Expansion of a Resistant Leukemic Clone as a Mechanism for Relapse in ALL While these data echoed previous findings suggestive of a preexisting resistant leukemic population, they were particularly valuable for investigating the relationship between the quantity of the relapse clone at diagnosis and patient outcome among the informative patients. For seven of the eight patients for whom the relapse clone was detected as a minor clone at diagnosis, the level of the relapse clone at diagnosis could be accurately quantified, the exception being patient B16 (Table 1). Notably, a significant inverse correlation was observed between the time to first relapse and the amount 1316
of the relapse clone present at diagnosis, with high levels predicting a shorter remission (r = -0.84, p = 0.018, Fig. 2A). For example, the leukemic populations of patients B6, B7 and B20 had the highest levels of the relapse clone at diagnosis and these patients had the shortest relapse-free survival (a mean of 13 months, compared to 24 months for the remaining four informative patients). The leukemic populations of patients A2 and A3 had the lowest levels of the relapse clone at diagnosis along with the longest relapse-free survival among these seven informative patients. While the relapse clone of patient B16 was detectable but at levels too low to allow accurate quantitation, it is interesting to note that this patient had the longest relapse-free survival among the eight patients for whom the relapse clone was detected in the diagnosis sample (Fig. 2A, open square). Furthermore, for the four patients in whom no relapse clone could be detected in the diagnosis marrow sample, the time to first relapse was significantly longer by comparison with the eight patients in whom the relapse clone was detected in the diagnosis sample (Fig. 2B; p = 0.0043). While an important next step will be to confirm these findings in a larger cohort, this study provides three strong lines of evidence for
Cell Cycle
2008; Vol. 7 Issue 10
Mechanism of relapse in leukemia
Table 1 Detection of relapse markers at diagnosis Proportion of relapse clone at diagnosis
31
VH1-69/D3-10/J6
1.0 x 10-5
A3
25
VH2-5/D3-3/J4
1.0 x 10-4
B6
8
VH4-39/D5-5/J4
8.7 x 10-3
B7
14
VH6-1/D6-13/J2
1.2 x 10-1
B14
16
Vγ11/Jγ1
9.0 x 10-4
B16
41
Vκ7/κdel
1.01 x 10-5 *
B19
23
VH6-1/D2-8/J6
4.5 x 10-4
B20
18
VH3-23/D6-6/J5
9.0 x 10-3
A1
43
VH2-48/D1-26/J4
ND
A4
43
VH2-5/D3-22/J4
ND
A5
47
VH3-15/D2-15/J4
ND
B8
37
VκInt/κdel
ND
UT E
.
A2
IST RIB
Relapse clone not detected
Clinical Implications for ALL Therapy and Risk Stratification
*, approximate level; ND = not detectable.
©
20
08
LA
ND
ES
BIO
SC
IEN CE
.D
ON
the “selection” hypothesis for relapsing clones (see Fig. 1B). Firstly, in concordance with a number of recent studies we found that relapse may indeed arise following expansion of a minor sub-population of leukemic cells present at diagnosis. Secondly, the existence of inherently resistant sub-clones at diagnosis is supported by the differential chemosensitivity between diagnostic sub-clones observed in one of the patients in the cohort.20 Thirdly, and most significantly, this was the first demonstration that, for a number of patients with a range of remission durations, the time to relapse depends upon the initial amount of the relapse clone, as marked by specific clonal rearrangements, present at diagnosis. Taken together, these data indicate that the biological characteristics necessary for relapse are already present in a small number of leukemia cells at the time of diagnosis in a proportion of patients. Further support for the presence of a low-abundance relapse-causing population at diagnosis came from a report, in the same journal issue, documenting the emergence of imatinib-resistant Ph+ ALL in the relapse samples of a number of patients.21 Interestingly, 10 of 21 patients with newly diagnosed Ph+ ALL harboured a minor leukemic clone with a kinase deficient mutant BCR-ABL and this mutation was found in the dominant leukemic population at relapse, although no investigation of the amount of the relapse clone present at diagnosis and its impact on length of remission was reported. In our study, for a subset of patients, the level of the relapse clone at diagnosis predicted the timing of relapse, from as early as eight months out to 41 months after diagnosis. However in the four patients for whom the relapse clone could not be detected by sensitive RQ-PCR in the diagnosis sample, relapse occurred at intervals between 37 and 47 months after diagnosis. It is possible that relapse in these four cases was due to expansion of an existing clone of intermediate resistance, or else due to acquisition of resistance following therapy as described in Figure 1A. However, given the strong correlation observed between the level of the relapse clone in the diagnosis
Our work also has important implications for MRD monitoring in ALL. In the majority of patients who relapse, the responsible leukemic sub-clone is likely to be present at diagnosis. In line with other studies,16,17,24-27 we observed various levels of marker stability and found that if sufficient markers are followed a common origin can be demonstrated for the leukemia at diagnosis and relapse, which reinforces prior recommendations to use at least two clonal immunoglobulin or T-cell receptor markers for MRD monitoring.24,28,29 However, since this sub-clone may be of low abundance yet still result in disease return, an early response to induction therapy may reflect the behaviour of only the major leukemic clone and hence not always be a reliable guide. Furthermore, this relatively resistant clone will most likely undergo some reduction during therapy and be more difficult to detect after remission induction, thus revealing another source of false negative results for post-induction MRD. In this way, limitations in the level of RQ-PCR sensitivity probably explain why post-induction MRD most effectively predicts early rather than late relapses,6,10 and may account for those cases highlighted in a previous study where MRD at 24 months after diagnosis was found to be independently predictive of relapse by comparison with postinduction MRD testing.9 Together, the findings highlight the need to detect as many antigen receptor rearrangements at diagnosis as practicable, to enhance the ability to monitor a small resistant subpopulation during the course of treatment. In the future, it may be more informative to follow all major and minor clones during the early phases of chemotherapy, mapping the relative responses of each clone to treatment. High risk of relapse may then be indicated by divergent quantitative responses of individual clones.
OT D
Patient Time to Relapse ID relapse (m) rearrangement Relapse clone detected
sample and the time to relapse, and the overlap in the range of time to relapse between the two groups, it remains possible that the relapse clone of these four patients was present at diagnosis but at levels below the detection limit of the technique used (Fig. 2A). The strong correlation between the quantity of relapse clone at diagnosis and time to relapse would suggest that if these relapse clones were indeed present at diagnosis, they would be present at a frequency below one cell in 106 which is the lower limit of detection with the majority of current RQ-PCR protocols. This question might be addressed following future developments and/or improvements in PCR methodology, or from propagation of patient leukemia cells obtained at diagnosis in vitro, or in murine xenograft models. For example, previous studies utilising engraftment of leukemia cells in immunocompromised mice have shown that even a minor clone, marked by a rearrangement of extremely low abundance, can selectively engraft and expand in mice.22,23 In the case of the late-relapse patients in our study, appearance of the relapse clone after serial passage of diagnostic material in NOD/SCID mice would confirm its presence in the primary diagnosis sample.
www.landesbioscience.com
Future Prospects: Exploiting the Molecular Profile of ALL Relapse The poor outcome commonly associated with patients that experience relapse highlights the need for new ways to predict relapse so that appropriate treatment interventions can be made early, during the first six months of therapy, and to identify new treatments that
Cell Cycle
1317
UT E IST RIB OT D
.D
ON
specifically target relapsed ALL. Accordingly, an intensive area of investigation involves generating gene expression signatures that distinguish leukemia cells at relapse from their diagnosis counterparts, with the hope of pinpointing key targets for therapy.30-32 In addition, expression profiling experiments comparing diagnosis specimens from patients who relapsed to those from patients who remained disease-free after treatment have sought to reveal pathways responsible for a “relapse-prone” phenotype.33-37 Such analyses have the potential to identify new therapeutic targets and may actually provide more useful information than approaches comparing diagnosis samples with corresponding relapse samples, the latter of which will have been obtained following exposure to chemotherapeutic agents.31 At the least, such “relapse-prone” profiles could be used to identify patients who warrant more intensive monitoring or additional therapy. Yeoh and colleagues were not able to identify a general signature predictive of relapse for all ALL patients, but could identify predictive signatures for certain ALL subtypes.37 A very recent study successfully identified a small set of genes that could predict early relapse of B-lineage ALL regardless of cytogenetic sub-types.34 Gene signatures predictive of outcome have also been successfully identified in T-lineage ALL.33,37 The data emerging from such studies encourage new ways of thinking about the biological mechanisms that lead to relapse, such as the cross-talk between tumour cells and the microenvironment. For example the aberrant expression of genes such as EMMPRIN (a potent extracellular matrix metalloproteinase inducer involved in tissue invasion and metastasis)30,33,35,37 could confer upon leukemia cells the ability to escape therapy by penetrating so-called “sanctuary sites”, only to later expand and cause relapse, or else may produce cells that are inherently more prone to dissemination. How is it that the diagnostic bone marrow specimen gives an indication of the propensity to relapse, given the observation that the relapse population may often represent only a minor proportion of the entire diagnostic sample? One way by which the initial leukemia could be ‘relapse-prone’ might be if its genetic profile at the time of disease presentation was one that favoured the generation of relatively resistant or more aggressive sub-clones, for example by possessing a high degree of genetic variation. In this case one would indeed expect to find fundamental differences at diagnosis between disease that goes on to relapse following treatment compared to that which is eliminated completely. Support for this idea comes from a recent study where the majority of genes perturbed in diagnosis samples of relapsed ALL patients were those important for the processes of cell division and DNA replication and repair.38 Alternatively, alterations in microenvironmental signalling patterns within the leukemic niche may confer potent pro-survival cues that contribute to the protection of tumour cells during therapy and facilitate their subsequent expansion at relapse. Clearly, pure populations of the relapse clone from patients would provide the ideal material for profiling experiments aimed at identifying relevant therapeutic targets for the eradication of ALL relapse. If the relapse clone could be isolated in the absence of chemotherapy, this would remove extraneous changes in expression that are perhaps not relevant to disease progression. An isolated observation of clonal selection during engraftment of primary patient ALL cells in NOD/ SCID mice provides some hope for future endeavours in this area. Xenografts in NOD/SCID mice have been shown to retain the
.
Mechanism of relapse in leukemia
©
20
08
LA
ND
ES
BIO
SC
IEN CE
Figure 2. Impact of presence of relapse clone at diagnosis on patient outcome. (A) Correlation between quantity of relapse clone present at diagnosis and time to first relapse in informative patients (solid squares), including one patient for whom the relapse clone was detectable but not quantifiable (open square), shown below the limit of quantitation for that assay (10-5, dotted line). Shown for comparison are 4 patients (open circles) for whom the relapse clone was below the limit of RQ-PCR detection (10-6, shaded area) at diagnosis. (B) Association between time to first relapse and detection of relapse clone at diagnosis. Patients were grouped according to whether the relapse clone could be detected by specific RQ-PCR analysis of the diagnosis specimen.
1318
same cytological, immunophenotypic and chemosensitivity profiles as the original patient ALL samples and therefore are an excellent pre-clinical model for ALL.23,39-41 In one case reported previously, when a diagnostic ALL specimen with an identifiable relapse marker was propagated in NOD/SCID mice and followed for three subsequent engraftments, the same clonal relapse marker emerged in the tertiary transplant as in the patient at relapse, without the need for the selective pressure of chemotherapy.23 This would suggest that the engraftment process in this case selected for some leukemic sub-clones but not others. While this phenomenon may apply only to those samples where the mechanism of escape from therapy in the patient correlates with the ability to engraft in the murine model (for example the capacity to remodel microenvironmental signalling, or to escape from immune surveillance through an enhanced ability for tissue invasion), if it holds true for other patient samples, such as the informative patients the Choi et al., study,20 it would represent a powerful way to select and characterise eventual relapse leukemia cell populations without the complication of chemotherapeutic exposure.
Cell Cycle
2008; Vol. 7 Issue 10
Mechanism of relapse in leukemia
References
©
20
08
LA
ND
ES
BIO
SC
.
IEN CE
.D
ON
1. Pui CH. Recent advances in childhood acute lymphoblastic leukemia. J Formos Med Assoc 2004; 103:85-95. 2. Redaelli A, Laskin BL, Stephens JM, Botteman MF, Pashos CL. A systematic literature review of the clinical and epidemiological burden of acute lymphoblastic leukaemia (ALL). Eur J Cancer Care (Engl) 2005; 14:53-62. 3. Cazzaniga G, Biondi A. Molecular monitoring of childhood acute lymphoblastic leukemia using antigen receptor gene rearrangements and quantitative polymerase chain reaction technology. Haematologica 2005; 90:382-90. 4. Pui CH, Relling MV, Downing JR. Acute lymphoblastic leukemia. N Engl J Med 2004; 350:1535-48. 5. Borowitz MJ, Pullen DJ, Shuster JJ, Viswanatha D, Montgomery K, Willman CL, Camitta B. Minimal residual disease detection in childhood precursor-B-cell acute lymphoblastic leukemia: relation to other risk factors. A Children’s Oncology Group study. Leukemia 2003; 17:1566-72. 6. Cave H, van der Werff ten Bosch J, Suciu S, Guidal C, Waterkeyn C, Otten J, Bakkus M, Thielemans K, Grandchamp B, Vilmer E. Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia. European Organization for Research and Treatment of Cancer—Childhood Leukemia Cooperative Group. N Engl J Med 1998; 339:591-8. 7. Coustan Smith E, Sancho J, Hancock ML, Boyett JM, Behm FG, Raimondi SC, Sandlund JT, Rivera GK, Rubnitz JE, Ribeiro RC, Pui CH, Campana D. Clinical importance of minimal residual disease in childhood acute lymphoblastic leukemia. Blood 2000; 96:2691-6. 8. Flohr T, Schrauder A, Cazzaniga G, Panzer Grumayer R, van der Velden VH, Fisher S, Stanulla M, Basso G, Niggli FK, Schafer BW, Sutton R, Koehler R, Zimmermann M, Valsecchi MG, Gadner H, Masera G, Schrappe M, Biondi A, van Dongen JJM, Bartram CR. Minimal residual disease (MRD)-directed risk stratification using real-time quantitative PCR analysis of immunoglobulin and T-cell receptor gene rearrangements in the international multicenter trial AIEOP-BFM all 2000 for childhood acute lymphoblastic leukemia (ALL). Leukemia 2008; Jan 31; [Epub ahead of print] PMID: 18239620. 9. Marshall GM, Haber M, Kwan E, Zhu L, Ferrara D, Xue C, Brisco MJ, Sykes PJ, Morley A, Webster B, Dalla Pozza L, Waters K, Norris MD. Importance of minimal residual disease testing during the second year of therapy for children with acute lymphoblastic leukemia. J Clin Oncol 2003; 21:704-9. 10. van Dongen JJ, Seriu T, Panzer Grumayer ER, Biondi A, Pongers Willemse MJ, Corral L, Stolz F, Schrappe M, Masera G, Kamps WA, Gadner H, van Wering ER, Ludwig WD, Basso G, de Bruijn MA, Cazzaniga G, Hettinger K, van der Does-van den Berg A, Hop WC, Riehm H, Bartram CR. Prognostic value of minimal residual disease in acute lymphoblastic leukaemia in childhood. Lancet 1998; 352:1731-8. 11. Szczepanski T. Why and how to quantify minimal residual disease in acute lymphoblastic leukemia? Leukemia 2007; 21:622-6. 12. van Dongen JJ, Langerak AW, Bruggemann M, Evans PA, Hummel M, Lavender FL, Delabesse E, Davi F, Schuuring E, Garcia Sanz R, van Krieken JH, Droese J, Gonzalez D, Bastard C, White HE, Spaargaren M, Gonzalez M, Parreira A, Smith JL, Morgan GJ, Kneba M, Macintyre EA. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 2003; 17:2257-317. 13. de Haas V, Verhagen OJ, von dem Borne AE, Kroes W, van den Berg H, van der Schoot CE. Quantification of minimal residual disease in children with oligoclonal B-precursor acute lymphoblastic leukemia indicates that the clones that grow out during relapse already have the slowest rate of reduction during induction therapy. Leukemia 2001; 15:134-40.
UT E
We thank Edward Kwan, Anita Bahar, Jodie Giles, Luciano Dalla Pozza and David Baker for their contributions. We also thank the National Health and Medical Research Council, The Cancer Council New South Wales, The Leukaemia Foundation, the Anthony Rothe Memorial Trust and The Children’s Leukaemia and Cancer Research Foundation, WA for their financial support.
IST RIB
Acknowledgements
14. Konrad M, Metzler M, Panzer S, Ostreicher I, Peham M, Repp R, Haas OA, Gadner H, Panzer-Grumayer ER. Late relapses evolve from slow-responding subclones in t(12;21)positive acute lymphoblastic leukemia: evidence for the persistence of a preleukemic clone. Blood 2003; 101:3635-40. 15. Li A, Zhou J, Zuckerman D, Rue M, Dalton V, Lyons C, Silverman LB, Sallan SE, Gribben JG. Sequence analysis of clonal immunoglobulin and T-cell receptor gene rearrangements in children with acute lymphoblastic leukemia at diagnosis and at relapse: implications for pathogenesis and for the clinical utility of PCR-based methods of minimal residual disease detection. Blood 2003; 102:4520-6. 16. Li AH, Rosenquist R, Forestier E, Lindh J, Roos G. Detailed clonality analysis of relapsing precursor B acute lymphoblastic leukemia: implications for minimal residual disease detection. Leuk Res 2001; 25:1033-45. 17. Peham M, Konrad M, Harbott J, Konig M, Haas OA, Panzer-Grumayer ER. Clonal variation of the immunogenotype in relapsed ETV6/RUNX1-positive acute lymphoblastic leukemia indicates subclone formation during early stages of leukemia development. Genes Chromosomes Cancer 2004; 39:156-60. 18. Brisco MJ, Sykes PJ, Dolman G, Hughes E, Neoh SH, Peng L, Snell LE, Toogood IR, Rice MS, Morley AA. Early resistance to therapy during induction in childhood acute lymphoblastic leukemia. Cancer Res 2000; 60:5092-6. 19. Zhu YM, Foroni L, McQuaker IG, Papaioannou M, Haynes A, Russell HH. Mechanisms of relapse in acute leukaemia: involvement of p53 mutated subclones in disease progression in acute lymphoblastic leukaemia. Br J Cancer 1999; 79:1151-7. 20. Choi S, Henderson MJ, Kwan E, Beesley AH, Sutton R, Bahar AY, Giles J, Venn NC, Pozza LD, Baker DL, Marshall GM, Kees UR, Haber M, Norris MD. Relapse in children with acute lymphoblastic leukemia involving selection of a preexisting drug-resistant subclone. Blood 2007; 110:632-9. 21. Pfeifer H, Wassmann B, Pavlova A, Wunderle L, Oldenburg J, Binckebanck A, Lange T, Hochhaus A, Wystub S, Bruck P, Hoelzer D, Ottmann OG. Kinase domain mutations of BCR-ABL frequently precede imatinib-based therapy and give rise to relapse in patients with de novo Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL). Blood 2007; 110:727-34. 22. Barabe F, Kennedy JA, Hope KJ, Dick JE. Modeling the initiation and progression of human acute leukemia in mice. Science 2007; 316:600-4. 23. Liem NL, Papa RA, Milross CG, Schmid MA, Tajbakhsh M, Choi S, Ramirez CD, Rice AM, Haber M, Norris MD, MacKenzie KL, Lock RB. Characterization of childhood acute lymphoblastic leukemia xenograft models for the preclinical evaluation of new therapies. Blood 2004; 103:3905-14. 24. Panzer Grumayer ER, Cazzaniga G, van der Velden VH, del Giudice L, Peham M, Mann G, Eckert C, Schrauder A, Germano G, Harbott J, Basso G, Biondi A, van Dongen JJ, Gadner H, Haas OA. Immunogenotype changes prevail in relapses of young children with TEL-AML1positive acute lymphoblastic leukemia and derive mainly from clonal selection. Clin Cancer Res 2005; 11:7720-7. 25. Szczepanski T, Flohr T, van der Velden VH, Bartram CR, van Dongen JJ. Molecular monitoring of residual disease using antigen receptor genes in childhood acute lymphoblastic leukaemia. Best Pract Res Clin Haematol 2002; 15:37-57. 26. Taub JW, Ge Y. The prenatal origin of childhood acute lymphoblastic leukemia. Leuk Lymphoma 2004; 45:19-25. 27. Wiemels JL, Cazzaniga G, Daniotti M, Eden OB, Addison GM, Masera G, Saha V, Biondi A, Greaves MF. Prenatal origin of acute lymphoblastic leukaemia in children. Lancet 1999; 354:1499-503. 28. Beishuizen A, Verhoeven MA, van Wering ER, Hahlen K, Hooijkaas H, van Dongen JJ. Analysis of Ig and T-cell receptor genes in 40 childhood acute lymphoblastic leukemias at diagnosis and subsequent relapse: implications for the detection of minimal residual disease by polymerase chain reaction analysis. Blood 1994; 83:2238-47. 29. Lo Nigro L, Cazzaniga G, Di Cataldo A, Pannunzio A, D’Aniello E, Masera G, Schiliro G, Biondi A. Clonal stability in children with acute lymphoblastic leukemia (ALL) who relapsed five or more years after diagnosis. Leukemia 1999; 13:190-5. 30. Beesley AH, Cummings AJ, Freitas JR, Hoffmann K, Firth MJ, Ford J, de Klerk NH, Kees UR. The gene expression signature of relapse in paediatric acute lymphoblastic leukaemia: implications for mechanisms of therapy failure. Br J Haematol 2005; 131:447-56. 31. Bhojwani D, Kang H, Moskowitz NP, Min DJ, Lee H, Potter JW, Davidson G, Willman CL, Borowitz MJ, Belitskaya-Levy I, Hunger SP, Raetz EA, Carroll WL. Biologic pathways associated with relapse in childhood acute lymphoblastic leukemia: a Children’s Oncology Group study. Blood 2006; 108:711-7. 32. Staal FJ, van der Burg M, Wessels LF, Barendregt BH, Baert MR, van den Burg CM, van Huffel C, Langerak AW, van der Velden VH, Reinders MJ, van Dongen JJ. DNA microarrays for comparison of gene expression profiles between diagnosis and relapse in precursor-B acute lymphoblastic leukemia: choice of technique and purification influence the identification of potential diagnostic markers. Leukemia 2003; 17:1324-32. 33. Gottardo NG, Hoffmann K, Beesley AH, Freitas JR, Firth MJ, Perera KU, de Klerk NH, Baker DL, Kees UR. Identification of novel molecular prognostic markers for paediatric T-cell acute lymphoblastic leukaemia. Br J Haematol 2007; 137:319-28. 34. Hoffmann K, Firth MJ, Beesley AH, Freitas JR, Ford J, Senanayake S, de Klerk NH, Baker DL, Kees UR. Prediction of relapse in paediatric pre-B acute lymphoblastic leukaemia using a three-gene risk index Br J Haematol 2008; 140:656-64. 35. Lanciotti M, D’Apolito M, Paolucci P, Dufour C. Chromosomal locus 19p13 as potential hotspot for aberrant gene expression in relapsed paediatric acute lymphoblastic leukaemia. Br J Haematol 2006; 135:274-5.
OT D
In summary, diverse experimental approaches have yielded significant insights into the mechanism of ALL relapse. These include the detailed molecular analysis of clinical material over the course of disease and the generation of relevant in vivo models to mimic leukemia initiation and progression in the presence or absence of therapy. The knowledge that leukemic cells harboring the characteristics required for ultimate relapse may be present in the leukemic population at diagnosis, rather than evolving in response to treatment, should set the scene for future investigations into treatment failure. In the interim, this information raises the alert for more careful monitoring of ALL patients at all stages, with the aim of providing more timely intensification of therapy.
www.landesbioscience.com
Cell Cycle
1319
Mechanism of relapse in leukemia
©
20
08
LA
ND
ES
BIO
SC
IEN CE
.D
ON
OT D
IST RIB
UT E
.
36. Winter SS, Jiang Z, Khawaja HM, Griffin T, Devidas M, Asselin BL, Larson RS. Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children’s Oncology Group. Blood 2007; 110:1429-38. 37. Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A, Cheng C, Campana D, Wilkins D, Zhou X, Li J, Liu H, Pui CH, Evans WE, Naeve C, Wong L, Downing JR. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002; 1:133-43. 38. Flotho C, Coustan-Smith E, Pei D, Cheng C, Song G, Pui CH, Downing JR, Campana D. A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia. Blood 2007; 110:1271-7. 39. Borgmann A, Baldy C, von Stackelberg A, Beyermann B, Fichtner I, Nurnberg P, Henze G. Childhood all blasts retain phenotypic and genotypic characteristics upon long-term serial passage in NOD/SCID mice. Pediatr Hematol Oncol 2000; 17:635-50. 40. Lock RB, Liem N, Farnsworth ML, Milross CG, Xue C, Tajbakhsh M, Haber M, Norris MD, Marshall GM, Rice AM. The nonobese diabetic/severe combined immunodeficient (NOD/ SCID) mouse model of childhood acute lymphoblastic leukemia reveals intrinsic differences in biologic characteristics at diagnosis and relapse. Blood 2002; 99:4100-8. 41. Nijmeijer BA, Mollevanger P, van Zelderen-Bhola SL, Kluin-Nelemans HC, Willemze R, Falkenburg JH. Monitoring of engraftment and progression of acute lymphoblastic leukemia in individual NOD/SCID mice. Exp Hematol 2001; 29:322-9.
1320
Cell Cycle
2008; Vol. 7 Issue 10
