Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia

Maher Albitar, Steven J. Potts, Francis J. Giles, Susan O'Brien, Michael Keating, Deborah Thomas, Charlotte Clarke, Iman Jilani, Christine Aguilar, Elihu Estey, Hagop Kantarjian

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

BACKGROUND. Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS. Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS. The best decision trees provided strong positive prediction of recurrence, with correct predictions 84% to 92% of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62% to 74% accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, β-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80% of test cases. CONCLUSIONS. Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.

Original languageEnglish (US)
Pages (from-to)1587-1594
Number of pages8
JournalCancer
Volume106
Issue number7
DOIs
StatePublished - Apr 1 2006

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Precursor Cell Lymphoblastic Leukemia-Lymphoma
Proteomics
Recurrence
Decision Trees
Bone Marrow
Protein Array Analysis
Cytogenetics
Anions
Lasers
Pediatrics
Therapeutics

Keywords

  • Acute lymphoblastic leukemia
  • ALL
  • Cytogenetics
  • Decision trees
  • Prognosis
  • Proteomics
  • Recurrence

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Albitar, M., Potts, S. J., Giles, F. J., O'Brien, S., Keating, M., Thomas, D., ... Kantarjian, H. (2006). Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia. Cancer, 106(7), 1587-1594. https://doi.org/10.1002/cncr.21770

Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia. / Albitar, Maher; Potts, Steven J.; Giles, Francis J.; O'Brien, Susan; Keating, Michael; Thomas, Deborah; Clarke, Charlotte; Jilani, Iman; Aguilar, Christine; Estey, Elihu; Kantarjian, Hagop.

In: Cancer, Vol. 106, No. 7, 01.04.2006, p. 1587-1594.

Research output: Contribution to journalArticle

Albitar, M, Potts, SJ, Giles, FJ, O'Brien, S, Keating, M, Thomas, D, Clarke, C, Jilani, I, Aguilar, C, Estey, E & Kantarjian, H 2006, 'Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia', Cancer, vol. 106, no. 7, pp. 1587-1594. https://doi.org/10.1002/cncr.21770
Albitar M, Potts SJ, Giles FJ, O'Brien S, Keating M, Thomas D et al. Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia. Cancer. 2006 Apr 1;106(7):1587-1594. https://doi.org/10.1002/cncr.21770
Albitar, Maher ; Potts, Steven J. ; Giles, Francis J. ; O'Brien, Susan ; Keating, Michael ; Thomas, Deborah ; Clarke, Charlotte ; Jilani, Iman ; Aguilar, Christine ; Estey, Elihu ; Kantarjian, Hagop. / Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia. In: Cancer. 2006 ; Vol. 106, No. 7. pp. 1587-1594.
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abstract = "BACKGROUND. Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS. Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS. The best decision trees provided strong positive prediction of recurrence, with correct predictions 84{\%} to 92{\%} of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62{\%} to 74{\%} accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, β-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80{\%} of test cases. CONCLUSIONS. Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.",
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AU - Thomas, Deborah

AU - Clarke, Charlotte

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N2 - BACKGROUND. Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS. Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS. The best decision trees provided strong positive prediction of recurrence, with correct predictions 84% to 92% of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62% to 74% accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, β-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80% of test cases. CONCLUSIONS. Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.

AB - BACKGROUND. Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS. Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS. The best decision trees provided strong positive prediction of recurrence, with correct predictions 84% to 92% of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62% to 74% accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, β-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80% of test cases. CONCLUSIONS. Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.

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