04hema- Innovative Leukemia and Lymphoma Therapy.pdf

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Library of Congress Cataloging-in-Publication Data
Innovative leukemia and lymphoma therapy / edited by Gertjan J. L.
Kaspers....[et al.].
p. ; cm. — (Basic and clinical oncology ; 35)
Includes bibliographical references and index.
ISBN-13: 978-0-8493-5083-2 (hardcover : alk. paper)
ISBN-10: 0-8493-5083-2 (hardcover : alk. paper) 1. Leukemia—
Treatment. 2. Lymphomas—Treatment. I. Kaspers, G. J. L., 1963- II. Series.
[DNLM: 1. Leukemia—therapy. 2. Lymphoma—therapy. 3. Therapies,
Investigational. W1 BA813W v.35 2008 / WH 250 I58 2008]
RC643.I46 2008
616.990
41906—dc22
2008006553
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Foreword
The outcome of therapy for leukemia and malignant lymphoma has improved
over the years, mainly in younger patients. Yet, there is no question that the
challenges in the area of developmental therapeutics have remained formidable.
These challenges relate to the patients who, from the start of treatment, fail to
respond to the currently available therapies or combinations of drugs. The
outlook of these primarily refractory patients is invariably dismal. Many of
the responder patients attaining an initial complete remission, unfortunately, will
finally present with relapse of disease. The relapses among the leukemias and
high-grade lymphomas usually occur early on, i.e., within the first two years.
Both groups, initial nonresponders and secondary failures, pose the notorious
difficulty of resistance to conventional therapy. These facts provide an overall
notion. Acquired somatic genetic abnormalities of the neoplasms provide keys to
the nature of the disease and offer important predictors of treatment failure. They
allow to pinpoint individual disease-specific features and distinguish variable
disease risks as well as identify those patients with the highest probability of
failure. The unmet therapeutic need is, by all standards, greatest among the large
population of older patients with hematological cancer in whom response rates
are comparatively low, relapse rates are high, and comorbidities prohibit the use
of classical chemotherapeutic agents at effective dose levels.
Scientists are on the way to discovering new drugs with different modes of
action that can overcome the limitations of today’s selection of drugs. Numerous
new drugs are currently in early clinical development with the aim of circum-
venting the clinical bottleneck of chemotherapy resistance. In the coming years,
several of these compounds are expected to settle as members of the standard
armamentarium of drugs available to the patient with a hematological tumor.
New drugs may be designed with the deliberate objective of affecting a known
molecular lesion or signaling pathway in the cancer cell, thus critically inhibiting
iii

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tumor cell survival. These therapeutic compounds may tackle distinct, molecu-
larly defined subtypes of leukemia or lymphoma, and one would anticipate that
their greater specificity will allow for application with enhanced efficacy and
reduced toxicity.
Currently, we are witnessing the development of diagnostic technologies
that directly impact decision making in the clinical management of patients with
hematological malignancies. These technologies relate, on the one hand, to more
precise tissue diagnosis and involve innovative genomic, proteomic, and immu-
nological techniques. On the other hand, they involve improved in vivo imaging
methods, enabling a better and more sensitive visualization of neoplastic
deposits in the body. These techniques, when appropriately validated for clinical
use, will enable the distinction of prognostic disease subcategories and allow for
a specific diagnosis according quantitative, sensitive, and objective parameters.
This type of information will guide therapeutic decisions at the outset of
treatment. It will also provide substantial insights that will be useful in
monitoring treatment effects throughout the therapeutic management of patients
and redirect treatment choice. An ambitious diagnostic approach makes sense if
there is a choice for the physician among a broader scale of available therapeutic
options. One of the major objectives of today’s molecular diagnostics relates to
the identification of new druggable targets for pharma developments.
Innovative Leukemia and Lymphoma Therapy appropriately and critically
deals with each of the issues and challenges as regards developmental thera-
peutics. The book highlights current, clinically relevant diagnostic strategies for
high-throughput diagnosis and disease response monitoring. The book covers, in
a series of individual chapters, a collection of overviews that highlight clinically
relevant novel therapeutic strategies in concise reviews. It also provides updates
on therapeutic compounds with new mechanisms of action that currently raise
intense interest and are in active development. This book comes as a timely
resource of information that furnishes a state-of-the-art and comprehensive
compendium, which will be of value to the interested clinician, researcher,
and student.
Bob Löwenberg
Erasmus University Medical Center
Rotterdam, The Netherlands
iv Foreword

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Preface
The treatment of leukemia and lymphoma is rapidly developing from conven-
tional chemotherapy toward a more tailored and targeted, innovative therapy.
However, conventional therapy is making progress as well. Targeted treatment
with increased efficacy and less side effects is becoming more and more a
reality, facilitated by fascinating developments such as oncogenomic studies and
sophisticated drug engineering. Knowledge on determinants of chemosensitivity
is also rapidly increasing. Together with pretreatment individualized tumor
response testing and with improved monitoring of treatment response by min-
imal residual disease measurements, treatment will indeed become more tailored
and individualized.
This book gives a complete and up-to-date overview of exciting new
treatment modalities in leukemia and lymphoma that have been introduced in the
clinic or will be introduced in the near future. Well-known international experts
summarize clinical studies on drugs such as tyrosine kinase inhibitors, mono-
clonal antibodies, proteasome inhibitors, farnesyl transferase inhibitors, hypo-
methylating agents, histone deacetylase inhibitors, mTOR targeting agents,
Notch pathway inhibitors, and inhibitors of cyclin-dependent kinases. The first
few chapters deal with methodological issues such as gene expression profiling
to detect new drug targets, individualized tumor response testing aiming at
selecting effective drugs, minimal residual monitoring to adapt treatment based
on actual treatment response, and statistical issues concerning clinical studies in
small subgroups of patients, while some discuss modulation of drug resistance
and improvements in allogeneic bone marrow transplantation. Other chapters
summarize targeting regulators of apoptosis, radioimmunotherapy, immunotherapy
by vaccination, gene-directed therapy, and anti-angiogenesis approaches. The
chapters provide a concise summary of the treatment rationale, of the pathways
that are involved, and of relevant preclinical research, whenever relevant.
v

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We recommend this well-illustrated, comprehensive book to students,
scientists, and clinicians with a special interest in innovative therapy who are
involved not only in research and/or treatment of leukemia and lymphoma in
particular, but in other malignancies as well.
G. J. L. Kaspers
Bertrand Coiffier
Michael C. Heinrich
Elihu Estey
vi Preface

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Contents
Foreword Bob Löwenberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1. Gene Expression Profiling to Detect New Treatment Targets
in Leukemia and Lymphoma: A Future Perspective . . . . . . . . . 1
Torsten Haferlach, Wolfgang Kern, and Alexander Kohlmann
2. Individualized Tumor Response Testing in Leukemia
and Lymphoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Andrew G. Bosanquet, Peter Nygren, and Larry M. Weisenthal
3. Minimal Residual Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Jacques J. M. van Dongen, Tomasz Szczepa
nski,
and Vincent H. J. van der Velden
4. New Methods for Clinical Trials: AML as an Example . . . . . . 85
Elihu Estey
5. Monoclonal Antibody Mediated Treatment in Acute Myeloid
Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Ch. Michel Zwaan and Marry M. van den Heuvel-Eibrink
6. Monoclonal Antibodies in the Treatment of Malignant
Lymphomas and Chronic Lymphocytic Leukemia . . . . . . . . 125
Bertrand Coiffier
vii

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7. Radioimmunotherapy of Hematological Malignancies . . . . . . 149
Tim Illidge and James Hainsworth
8. Differentiation Induction in Acute Promyelocytic Leukemia . . . . 185
Adi Gidron and Martin S. Tallman
9. DNA Methylation and Epigenetics: New Developments
in Biology and Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Jesus Duque, Michael L€
ubbert, and Mark Kirschbaum
10. The Emerging Role of Histone Deacetylase Inhibitors
in the Treatment of Lymphoma . . . . . . . . . . . . . . . . . . . . . . 233
Matko Kalac and Owen A. O’Connor
11. Antileukemic Treatment Targeted at Apoptosis Regulators . . . 257
Simone Fulda and Klaus-Michael Debatin
12. Angiogenesis in Hematological Malignancies . . . . . . . . . . . . . 283
Alida C. Weidenaar, Hendrik J. M. de Jonge, Arja ter Elst,
and Evelina S. J. M. de Bont
13. Nucleic Acid-Based, mRNA-Targeted Therapeutics
for Hematologic Malignancies . . . . . . . . . . . . . . . . . . . . . . . . 311
Alan M. Gewirtz
14. Active Specific Immunization by the Use of Leukemic Dendritic
Cell Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Ilse Houtenbos, Gert J. Ossenkoppele,
and Arjan A. van de Loosdrecht
15. CDK Inhibitors in Leukemia and Lymphoma . . . . . . . . . . . . 353
Yun Dai and Steven Grant
16. FLT3: A Receptor Tyrosine Kinase Target in Adult
and Pediatric AML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Mark Levis, Patrick Brown, and Donald Small
17. Treatment of Chronic Myeloid Leukemia with Bcr-Abl
Kinase Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
Michael J. Mauro and Michael C. Heinrich
18. Tyrosine Kinase Inhibitors: Targets Other Than FLT3,
BCR-ABL, and c-KIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Suzanne R. Hayman and Judith E. Karp
viii Contents

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19. Tyrosine Phosphatases as New Treatment Targets
in Acute Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . 449
I. Hubeek, K. Hoorweg, J. Cloos, and G. J. L. Kaspers
20. Proteasome and Protease Inhibitors . . . . . . . . . . . . . . . . . . . 469
N. E. Franke, J. Vink, J. Cloos, and G. J. L. Kaspers
21. Farnesyltransferase Inhibitors: Current and Prospective
Development for Hematologic Malignancies . . . . . . . . . . . . . 491
Judith E. Karp
22. Targeting Notch Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . 513
Jennifer O’Neil and A. Thomas Look
23. mTOR Targeting Agents for the Treatment of Lymphoma
and Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
Andrea E. Wahner Hendrickson, Thomas E. Witzig,
and Scott H. Kaufmann
24. Allogeneic Hematopoietic Cell Transplantation After
Nonmyeloablative Conditioning . . . . . . . . . . . . . . . . . . . . . . 539
Frédéric Baron, Frederick R. Appelbaum, and Brenda M. Sandmaier
25. Modulation of Classical Multidrug Resistance and
Drug Resistance in General . . . . . . . . . . . . . . . . . . . . . . . . . 563
Branimir I. Sikic
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
Contents ix

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Contributors
Frederick R. Appelbaum Fred Hutchinson Cancer Research Center and The
University of Washington, Seattle, Washington, U.S.A.
Frédéric Baron Fred Hutchinson Cancer Research Center, Seattle, Washington,
U.S.A.
Andrew G. Bosanquet Bath Cancer Research, Royal United Hospital, Bath, U.K.
Patrick Brown Sidney Kimmel Comprehensive Cancer Center at Johns
Hopkins, Baltimore, Maryland, U.S.A.
J. Cloos Department of Pediatric Oncology/Hematology, VU University
Medical Center, Amsterdam, The Netherlands
Bertrand Coiffier Hematology Department, Hospices Civils de Lyon and
Claude Bernard University, Pierre-Benite, France
Yun Dai Department of Medicine, Virginia Commonwealth University and
Massey Cancer Center, Richmond, Virginia, U.S.A.
Evelina S. J. M. de Bont Department of Pediatric Oncology/Hematology,
University Medical Center Groningen, University of Groningen, Groningen,
The Netherlands
Hendrik J. M. de Jonge Department of Pediatric Oncology/Hematology,
The Netherlands
Klaus-Michael Debatin University Children’s Hospital, Ulm, Germany
Jesus Duque Department of Hematology/Oncology, University Medical
Center Freiburg, Freiburg, Germany
xi

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Elihu Estey Division of Hematology, University of Washington Medical
Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
N. E. Franke Department of Pediatric Oncology/Hematology, VU University
Simone Fulda University Children’s Hospital, Ulm, Germany
Alan M. Gewirtz Division of Hematology/Oncology, Department of Medicine
Abramson Family Cancer Research Institute, University of Pennsylvania
School of Medicine, Philadelphia, Pennsylvania, U.S.A.
Adi Gidron Division of Hematology/Oncology, Department of Medicine,
Northwestern University Feinberg School of Medicine and The Robert H. Lurie
Comprehensive Cancer Center of Northwestern University, Chicago, Illinois, U.S.A.
Steven Grant Department of Medicine, Biochemistry, and Pharmacology,
Virginia Commonwealth University and Massey Cancer Center, Richmond,
Virginia, U.S.A.
Torsten Haferlach Munich Leukemia Laboratory, Munich, Germany
James Hainsworth Paterson Institute of Cancer Research, School of
Medicine, University of Manchester, Manchester, U.K.
Suzanne R. Hayman Division of Hematology, Department of Medicine,
Mayo Clinic, Rochester, Minnesota, U.S.A.
Michael C. Heinrich Center for Hematologic Malignancies and Departments
of Medicine and Cell and Developmental Biology, Oregon Cancer Institute,
Oregon Health Science University and Portland VA Medical Center, Oregon
Health Science University, Portland, Oregon, U.S.A.
K. Hoorweg Department of Pediatric Oncology/Hematology, VU University
Ilse Houtenbos Department of Hematology, VU University Medical Center,
Amsterdam, The Netherlands
I. Hubeek Department of Pediatric Oncology/Hematology, VU University
Tim Illidge Paterson Institute of Cancer Research, School of Medicine,
University of Manchester, Manchester, U.K.
Matko Kalac Herbert Irving Comprehensive Cancer Center, The New York
Presbyterian Hospital, Columbia University, New York, New York, U.S.A.
Judith E. Karp Division of Hematologic Malignancies, Johns Hopkins Sidney
Kimmel Comprehensive Cancer Center, Baltimore, Maryland, U.S.A.
xii Contributors

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G. J. L. Kaspers Department of Pediatric Oncology/Hematology, VU
University Medical Center, Amsterdam, The Netherlands
Scott H. Kaufmann Department of Molecular Pharmacology and
Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, U.S.A.
Wolfgang Kern Munich Leukemia Laboratory, Munich, Germany
Mark Kirschbaum Division of Hematology and Hematopoietic Cell
Transplantation, City of Hope Comprehensive Cancer Center, Duarte,
California, U.S.A.
Alexander Kohlmann Roche Molecular Systems, Pleasanton, California, U.S.A.
Michael L€
ubbert Department of Hematology/Oncology, University Medical
Center Freiburg, Freiburg, Germany
Mark Levis Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins,
Baltimore, Maryland, U.S.A.
A. Thomas Look Department of Pediatric Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts, U.S.A.
Michael J. Mauro Center for Hematologic Malignancies, Oregon Cancer
Institute, Oregon Health Science University, Portland, Oregon, U.S.A.
Peter Nygren Department of Oncology, Radiology, and Clinical Immunology,
University Hospital, Uppsala, Sweden
Owen A. O’Connor Herbert Irving Comprehensive Cancer Center, The
New York Presbyterian Hospital, Columbia University, New York, New York,
U.S.A.
Jennifer O’Neil Department of Pediatric Oncology, Dana-Farber Cancer
Institute, Boston, Massachusetts, U.S.A.
Gert J. Ossenkoppele Department of Hematology, VU University Medical
Center, Amsterdam, The Netherlands
Brenda M. Sandmaier Fred Hutchinson Cancer Research Center and The
University of Washington, Seattle, Washington, U.S.A.
Branimir I. Sikic Oncology Division, Department of Medicine, Stanford
University School of Medicine, Stanford, California, U.S.A.
Donald Small Sidney Kimmel Comprehensive Cancer Center at Johns
Hopkins, Baltimore, Maryland, U.S.A.
Tomasz Szczepa
nski Department of Immunology, Erasmus MC, University
Medical Center Rotterdam, Rotterdam, The Netherlands, and Department of
Pediatric Hematology and Oncology, Medical University of Silesia, Zabrze,
Poland
Contributors xiii

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Martin S. Tallman Division of Hematology/Oncology, Department of
Medicine, Northwestern University Feinberg School of Medicine and The
Robert H. Lurie Comprehensive Cancer Center of Northwestern University,
Chicago, Illinois, U.S.A.
Arja ter Elst Department of Pediatric Oncology/Hematology,
The Netherlands
Marry M. van den Heuvel-Eibrink Department of Pediatric Oncology/
Hematology, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The
Netherlands
Arjan A. van de Loosdrecht Department of Hematology, VU University
Vincent H. J. van der Velden Department of Immunology, Erasmus MC,
University Medical Center Rotterdam, Rotterdam, The Netherlands
Jacques J. M. van Dongen Department of Immunology, Erasmus MC,
University Medical Center Rotterdam, Rotterdam, The Netherlands
J. Vink Department of Pediatric Oncology/Hematology, VU University
Andrea E. Wahner Hendrickson Department of Medicine, Mayo Clinic,
Rochester, Minnesota, U.S.A.
Alida C. Weidenaar Department of Pediatric Oncology/Hematology,
The Netherlands
Larry M. Weisenthal Weisenthal Cancer Group, Huntington Beach,
California, U.S.A.
Thomas E. Witzig Department of Medicine, Mayo Clinic, Rochester,
Minnesota, U.S.A.
Ch. Michel Zwaan Department of Pediatric Oncology/Hematology, Erasmus
MC/Sophia Children’s Hospital, Rotterdam, The Netherlands
xiv Contributors

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BASIC AND CLINICAL ONCOLOGY
Series Editor
Bruce D. Cheson
Professor of Medicine and Oncology
Head of Hematology
Georgetown University
Lombardi Comprehensive Cancer Center
Washington, D.C.
1. Chronic Lymphocytic Leukemia: Scientific Advances and Clinical
Developments, edited by Bruce D. Cheson
2. Therapeutic Applications of Interleukin-2, edited by Michael B.
Atkins and James W. Mier
3. Cancer of the Prostate, edited by Sakti Das and E. David Crawford
4. Retinoids in Oncology, edited by Waun Ki Hong and Reuben Lotan
5. Filgrastim (r-metHuG-CSF) in Clinical Practice, edited by George
Morstyn and T. Michael Dexter
6. Cancer Prevention and Control, edited by Peter Greenwald,
Barnett S. Kramer, and Douglas L. Weed
7. Handbook of Supportive Care in Cancer, edited by Jean Klastersky,
Stephen C. Schimpff, and Hans-Jörg Senn
8. Paclitaxel in Cancer Treatment, edited by William P. McGuire
and Eric K. Rowinsky
9. Principles of Antineoplastic Drug Development and Pharmacology,
edited by Richard L. Schilsky, G
erard A. Milano, and Mark J. Ratain
10. Gene Therapy in Cancer, edited by Malcolm K. Brenner and Robert
C. Moen
11. Expert Consultations in Gynecological Cancers, edited by Maurie
Markman and Jerome L. Belinson
12. Nucleoside Analogs in Cancer Therapy, edited by Bruce D. Cheson,
Michael J. Keating, and William Plunkett
13. Drug Resistance in Oncology, edited by Samuel D. Bernal
14. Medical Management of Hematological Malignant Diseases,
edited by Emil J Freireich and Hagop M. Kantarjian
15. Monoclonal Antibody-Based Therapy of Cancer, edited by Michael
L. Grossbard
16. Medical Management of Chronic Myelogenous Leukemia, edited
by Moshe Talpaz and Hagop M. Kantarjian

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17. Expert Consultations in Breast Cancer: Critical Pathways and
Clinical Decision Making, edited by William N. Hait, David A. August,
and Bruce G. Haffty
18. Cancer Screening: Theory and Practice, edited by Barnett S.
Kramer, John K. Gohagan, and Philip C. Prorok
19. Supportive Care in Cancer: A Handbook for Oncologists: Second
Edition, Revised and Expanded, edited by Jean Klastersky, Stephen
C. Schimpff, and Hans-Jörg Senn
20. Integrated Cancer Management: Surgery, Medical Oncology, and
Radiation Oncology, edited by Michael H. Torosian
21. AIDS-Related Cancers and Their Treatment, edited by Ellen
G. Feigal, Alexandra M. Levine, and Robert J. Biggar
22. Allogeneic Immunotherapy for Malignant Diseases, edited by John
Barrett and Yin-Zheng Jiang
23. Cancer in the Elderly, edited by Carrie P. Hunter, Karen A. Johnson,
and Hyman B. Muss
24. Tumor Angiogenesis and Microcirculation, edited by Emile E. Voest
and Patricia A. D’Amore
25. Controversies in Lung Cancer: A Multidisciplinary Approach, edited
by Benjamin Movsas, Corey J. Langer, and Melvyn Goldberg
26. Chronic Lymphoid Leukemias: Second Edition, Revised and
Expanded, edited by Bruce D. Cheson
27. The Myelodysplastic Syndromes: Pathology and Clinical Manage-
ment, edited by John M. Bennett
28. Chemotherapy for Gynecological Neoplasms: Current Therapy and
Novel Approaches, edited by Roberto Angioli, Pierluigi Benedetti
Panici, John J. Kavanagh, Sergio Pecorelli, and Manuel Penalver
29. Infections in Cancer Patients, edited by John N. Greene
30. Endocrine Therapy for Breast Cancer, edited by James N. Ingle and
Mitchell Dowsett
31. Anemia of Chronic Disease, edited by Guenter Weiss, Victor
R. Gordeuk, and Chaim Hershko
32. Cancer Risk Assessment, edited by Peter G. Shields
33. Thrombocytopenia, edited by Keith R. McCrae
34. Treatment and Management of Cancer in the Elderly, edited by
Hyman B. Muss, Carrie P. Hunter, and Karen A. Johnson
35. Innovative Leukemia and Lymphoma Therapy, edited by G. J. L.
Kaspers, Bertrand Coiffier, Michael C. Heinrich, and Elihu Estey

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22]
1
Gene Expression Profiling to Detect
New Treatment Targets in Leukemia
and Lymphoma: A Future Perspective
Torsten Haferlach and Wolfgang Kern
Munich Leukemia Laboratory, Munich, Germany
Alexander Kohlmann
Roche Molecular Systems, Pleasanton, California, U.S.A.
INTRODUCTION
The standard methods for establishing the diagnosis and prognosis of acute
leukemias and lymphomas are cytomorphology and cytochemistry in combina-
tion with multiparameter immunophenotyping. However, cytogenetics, fluores-
cence in situ hybridization (FISH), and polymerase chain reaction (PCR)-based
assays add important information with respect to biologically defined and
prognostically relevant subgroups. Together, a combination of different methods
allows a comprehensive diagnosis with relevant clearly defined subentities. It also
leads to a better understanding of the respective clinical course of defined disease
subtypes and to a more or less disease-specific therapeutic approach. However, not
all patients achieve complete remission during treatment, and many of those who
do, later develop relapse and treatment-resistant disease. To overcome these
problems, the microarray technology, which quantifies gene expression intensities
of thousands of genes in a single analysis, holds the potential to become an essential
tool for a strictly molecularly defined classification of leukemias and lymphomas.
1

22]
It may therefore be used at first as a novel routine method for diagnostic approaches
in the near future (1). But even more importantly, it will also reveal new genetic and
therapeutically relevant markers and should guide the search for new targets. Gene
expression profiling will also improve patient selection to test therapeutic
hypothesis most efficiently and may help define dose and schedule determina-
tion. This chapter outlines the major steps for gene expression profiling analyses
to approach these different goals by starting at a better diagnostic character-
ization of leukemias and lymphomas hopefully ending up with new targets for
individual treatment of the respective patients.
MICROARRAYS AND THE ERA OF FUNCTIONAL GENOMICS
Both biology and medicine are undergoing a revolution that is based on the
accelerating determination of DNA sequences, including the completion of whole
genomes of a growing number of organisms (2). In parallel to the sequencing efforts,
a wide range of technologies with tremendous potential has been achieved that can
take advantage of the vast quantity of genetic information being now available. The
field of functional genomics seeks to devise and apply these technologies, such as
microarrays, to analyze the full complement of genes and proteins encoded by an
organism to understand the functions of genes and proteins (3) (Fig. 1).
Figure 1 Different types of microarray platforms. Microarray platforms vary according to
the solid support used (such as glass slides or silicon wafers), the surface modifications with
various substrates, the type and length of DNA fragments on the array (such as cDNA or
oligonucleotides), whether the gene fragments are presynthesized and deposited, or synthe-
sized in situ, the machinery used to place the fragments on the array (such as ink-jet printing,
spotting, mask, or micromirror-based in situ synthesis), and the method of sample preparation.
Currently, combinations of these variables are used to generate two main types of microarrays:
spotted glass slide arrays (right) and in situ synthesized DNA-oligonucleotide arrays (left).
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Glass Slide Microarrays
Glass slide microarrays were first produced in Patrick Brown’s laboratory at
Stanford University (4). In glass slide microarray studies, ribonucleic acid
(RNA) species from the test sample and from the reference sample are studied
pairwise as an equivalent mixture in which the control RNA is the reference for
expressing the gene transcript levels in the target sample (Fig. 1). Various direct
and indirect labeling methods for the sample have been developed (5). The
majority of expression analysis labeling protocols is based on the reverse tran-
scription of mRNA, either from highly purified poly(A) mRNA or total RNA
extracts and often include amplification steps. In most protocols, one sample is
labeled with the Cy3 (green) fluorochrome, the other with Cy5 (red). The labeled
cRNA molecules hybridize to the corresponding cDNA or long oligonucleotides,
of which the exact position on the array is known. The binding of the target to
the probe is detected by scanning the array, typically using either a scanning
confocal laser or a charge coupled device (CCD) camera-based reader. After
scanning, software calculations provide the ratios between green and red fluo-
rescence for each spot, corresponding to the relative abundance of mRNA from a
particular gene in the target sample versus the reference sample.
However, the technical difficulties in the reproducible production of glass
slide microarrays should not be underestimated (5). Much of this variation is
introduced systematically during the spotting of the DNA onto the slide surface,
and many of the initial cDNA clone sets were compromised by contamination
with T1 phage, multiple clones in individual wells, and incorrect sequence
assignment. Thus, given the lack of a gold standard for the production of glass
slide microarrays using current technologies, there is a high degree of variation
in the quality of data derived from glass slide microarray experiments. This poor
reproducibility not only adds to the cost of a given study but also leads to data
sets that are difficult to interpret.
MICROARRAYS AS AN INNOVATIVE TECHNIQUE
TO DETECT NEW TARGETS
For several reasons many investigations using microarrays for biological
approaches today are performed on the whole genome Affymetrix U133 set
(HG-U133A and HG-U133B or the HG-U133 2.0 plus array; Affymetrix, Santa
Clara, California, U.S.). A detailed up-to-date description on sequences and
probe selection rules is available as technical note from the manufacturer (www
.affymetrix.com).
Affymetrix HG-U133A and HG-U133B Microarrays
The U133 two-array set provides comprehensive coverage of well-substantiated
genes in the human genome. It can be used to analyze the expression level of
Gene Expression Profiling 3

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39,000 transcripts and variants, including greater than 33,000 human genes. The
two arrays comprise more than 45,000 probe sets and 1,000,000 distinct oligo-
nucleotide features. The sequences from which these probe sets were derived
were selected from GenBank, dbEST, and RefSeq. The sequence clusters were
created from the UniGene database (Build 133, April 20, 2001) and then refined
by analysis and comparison with a number of other publicly available databases,
including the Washington University EST trace repository and the University of
California, Santa Cruz, Golden-Path human genome database (April 2001
release). In addition, an advanced understanding of probe uniqueness and
hybridization characteristics allowed an improved selection of probes based on
predicted behavior. The U133 chip design uses a multiple linear regression
model that was derived from a thermodynamic model of nucleic acid duplex
formation. This model predicts probe binding affinity and linearity of signal
changes in response to varying target concentrations. The two arrays are man-
ufactured as standard format arrays with a feature size of 18 mm and use 11 probe
pairs per sequence. The oligonucleotide length is 25 mer.
Human Genome U133 Plus 2.0 Array
In addition to all the sequences represented on the HG-U133A and HG-U133B
two-array set, the HG-U133 Plus 2.0 microarray also covers 9921 new probe sets
representing approximately 6500 new genes. These gene sequences were
selected from GenBank, dbEST, and RefSeq. Sequence clusters were created
from the UniGene database (Build 159, January 25, 2003) and refined by
analysis and comparison with a number of other publicly available databases,
including the Washington University EST trace repository and the NCBI human
genome assembly Build 31 (www.affymetrix.com). Thus, in using this com-
prehensive whole human genome expression array, an extensive coverage of the
human genome is reached. HG-U133 Plus 2.0 microarrays are manufactured as
standard format arrays with more than 54,000 probe sets of a feature size of
11 mm and use 11 probe pairs per sequence. The oligonucleotide length is 25 mer.
MICROARRAY DATA ANALYSIS
A wide range of approaches is available for gleaning insights from the data
obtained from transcriptional profiling. Data analyses are performed by two
different approaches, i.e., the supervised approach and the unsupervised
approach (Fig. 2). Unsupervised analyses are used to test the hypothesis whether
specific characteristics, e.g., genetic aberrations, are also reflected at the level of
gene expression signatures. Supervised analyses identify a minimal set of genes
that could be used to stratify those patients after a training of classification
engines (6–8). The gene lists from supervised analyses can also be further
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interpreted in terms of underlying biology. For all gene expression profiles,
master data tables have to be maintained. In these tables, rows represent all genes
for which data have been collected and columns represent microarray experi-
ments from individual patients. Each cell represents the measured fluorescence
intensity from the corresponding target probe set on the microarray. Before
analyzing the data, it is a routine procedure to normalize the data. This pro-
cedure is a mandatory step in the data-mining process to appropriately compare
the measured gene expression levels. U133 set microarray signal intensity
Figure 2 Overview about a common workflow to analyze microarray data. After
preparation of corresponding data sets from the main master table, the data are analyzed
either unsupervised or supervised. Unsupervised analyses are performed by hierarchical
clustering or principal component analysis. In the supervised analyses, differentially
expressed genes can be identified by various methods and selected for further inter-
pretations, e.g., visualization by hierarchical clustering, principal component analysis,
plotting as bar graphs, or generation of biological networks. In addition, differentially
expressed genes can be selected for classification tasks where several different machine-
learning approaches have to be applied.

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values can be normalized by scaling the raw data intensities to a common target
intensity using a recommended mask file.
Some Examples of Software to Identify Genes of Interest
Several software packages are used for principal data acquisition (GCOS),
storage (MicroDB), and analysis (DMT). The following tables give only some
examples to approach data. Individual gene expression profiles can also further
be prepared as Microsoft Excel tables.
Software Source Internet
GCOS Affymetrix, Inc. www.affymetrix.com/support/
MicroDB Affymetrix, Inc. www.affymetrix.com/support/
DMT Affymetrix, Inc. www.affymetrix.com/support/
The following packages can be applied for the identification of differ-
entially expressed genes and classification:
SAM Stanford University www-stat.stanford.edu/~tibs/SAM/
index.html
Bioconductor Open source www.bioconductor.org
q-Value University of
Washington
faculty.washington.edu/~jstorey/qvalue/
LIBSVM National Taiwan
University
www.csie.ntu.edu.tw/~cjlin/libsvm/
SAM is available as Microsoft Excel Add-in (9). Bioconductor is an open
source and open development software project for the analysis and comprehension
of genomic data. Bioconductor packages provide statistical and graphical
methodologies for analyzing genomic data. LIBSVM (Version 2.6) is a software
solution for SVM-based classification. The q-value software takes a list of p-values
resulting from the simultaneous testing of many hypotheses and estimates their
q-values (10). In addition, further third party software packages can be used for
statistical analyses and data visualization.
SPSS SPSS, Inc. www.spss.com/
Pathways Analysis Ingenuity Systems www.ingenuity.com
GeneMaths Applied Maths, Inc. www.applied-maths.com
Genomics Suits Partek, Inc. www.partek.com/
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Tools for Pathway Analyses to Detect New Targets and Correlations
The identification of diagnostic, prognostic, or therapeutic markers in leukemia
and lymphoma following microarray experiments and their biostatistical read outs
have to then focus on the discovery of important pathways in these tumors. Several
programs exist in order to identify pathways involved. These include Pathway
Assist (http://www.ariadnegenomics.com/products/pathway.html), DAVID (http://
apps1.niaid.nih.gov/david/), and Ingenuity (http://www.ingenuity.com/). As one
example, Ingenuity enables researchers to model, analyze, and understand complex
biological systems foundational to human health and disease. This includes
pathways analysis software and knowledge databases for biologists and bio-
statisticians and enterprise knowledge management infrastructure. Today, Ingenuity
is a useful knowledge base of biological networks with curated relationships between
proteins, genes, complexes, cells, tissues, drugs, and diseases.
Increasingly, also bioinformaticians are interested in developing analytical
tools that help scientists interpret experimental data especially in the context of
pathways and biological systems. These analytical tools have broad application
throughout research and development, from validating targets by uncovering
disease-related pathways to predicting pathways perturbed by therapeutic com-
pounds. As one example in Ingenuity, a broad genome-wide coverage of over
25,900 mammalian genes (11,100 human, 5500 rat, and 9300 mouse) can be
found and millions of pathway interactions extracted from literature are managed
interactively and web based.
At a basic level, an understanding of functions and pathways associated with
genes identified within an early-stage candidate region may assist in prioritizing
portions of this region for further investigation, e.g., targeted association using
higher densities of single nucleotide polymorphisms (SNPs). This type of approach
may even assist in identifying which genes to resequence in an attempt to identify
further SNPs for association studies. This is achievable now with the ability to
upload, for example, Affymetrix SNP identifiers directly into pathway software
such as Ingenuity. Future developments may increase the mapping coverage of
SNPs beyond the simple 1:1 gene to SNP mapping available today.
Beyond this, future functionality may even allow for the correlation
between multiple regions of the genome identified at a functional level and
findings of a genetic association study that identifies multiple, low scoring
regions. Previously, these may not have warranted further investigation based
solely on association scores. However, functional, process, pathway, or disease
annotations may implicate multiple regions as being relevant to a particular
phenotype by virtue of their compound effect. Evidence is already emerging
from the HapMap project that there are significant SNPs that are genetically
indistinguishable across large regions of individual chromosomes or even dif-
ferent chromosomes. It is anticipated that further development of software and
pathway analyses tools to approach the huge sets of data generated in microarray
experiments will lead to deeper insights.

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DETECTION OF NEW TARGETS IN LEUKEMIA AND LYMPHOMA
As has been outlined before, gene expression profiling has been extensively used
for tumor classification (8,11–15) and is on the way to add important information
to predict response to therapy as well as for outcome in leukemia and lymphoma
patients. As these data are not in the focus of this article, they will only be cited,
if they add information also for new target detection.
Furthermore, there are only limited efforts yet to incorporate microarrays
into clinical trials in hematology and oncology because of several reasons: (1)
prospective sample acquisition parallel to the gold standard diagnostic proce-
dures is needed, (2) standardized equipment and software has to be used, (3)
experienced scientists and technicians with respect to microarray analyses have
to be involved, and (4) funding is mostly lacking and would be best if academic
institutions and industry combine efforts. Other factors like intra-laboratory and
inter-laboratory comparability have also to be taken into account.
This leads to the following relation according to Weeraratna (16): More
than 9000 references are available that concern microarrays, but only around
20 are clinical trials, and less than 10 of these pertain to cancer. As currently no
single prospective trial has been conducted to our knowledge to address the use
of microarrays within a clinical trial in leukemia and lymphoma, we only can
rely on information that was published in papers referring to diagnostic or
prognostic questions. On the basis of their findings, some preliminary statements
can also be made for the use of gene expression profiling to define new targets
and drugs in leukemia and lymphoma (17). The following chapters will comment
on these aspects and will be subdivided disease specifically.
Detection of New Targets in Lymphoma
Alizadeh et al. (13) defined distinct subtypes of diffuse large B-cell lymphoma
(DLBCL) by specific gene expression signatures. Although this paper mostly
focuses on newly defined biological subgroups of DLBCL, different prognosis
was also detected. This again leads to the detection of genes that are responsible
not only for a better and novel subclassification but also transfer into striking
differences in prognosis if patients are treated uniformly. Thus, the authors
concluded that a respective gene expression pattern and the IPI score for NHL in
combination will guide therapeutic decisions including bone marrow trans-
plantation as one option for high-risk patients. Furthermore, expression profiling
may also help to detect homogeneous groups of patients to improve the likeli-
hood of observing treatment efficacy in specific disease entities. This study was
the first to show that the two DLBCL subgroups differentially expressed entire
transcriptional modules composed of hundreds of genes. Polo et al. identified
a discrete subset of DLBCL that are reliant on Bcl6 signaling and uniquely
sensitive to Bcl6 inhibitors (18). Therefore, successful new therapeutics may be
aimed at the upstream signal-transducing molecules and further investigations
are needed.
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Microarrays have also been used to study the targets of c-Myc, a tran-
scription factor that plays a role in Burkitt’s lymphoma as c-Myc is involved in
the chromosomal translocation t(8;14). In this study genomic targets including
genes involved in cell cycle, cytoskeletal organization, cell growth, and adhesion
were identified (19). However, these structures have to be tested again as drug
targets after having been detected by gene expression profiling.
Detection of New Targets in Acute Myeloid Leukemia
Yagi et al. (20) analyzed 54 pediatric acute myeloid leukemia (AML) using
Affymetrix U95A arrays and focused on the reproducibility of some FAB sub-
types and especially on gene patterns to predict outcome. After unsupervised
clustering, they were able to differentiate patients with t(8;21) from those with
inv(16) and from those demonstrating an AML M4/5 or AML M7 phenotype or
immunophenotype by specific gene expression signatures. Within this unsu-
pervised analysis, no specific profile was found that correlated to the prognosis
of the patients. Since the inclusion of further cases with other FAB subtypes and
cytogenetic abnormalities (no karyotype was available in 9 of 54 cases) resulted
in an increased heterogeneity, the authors restricted their further analyses to the
genetically and morphologically better-defined subentities. For further calcula-
tion, data were analyzed and supervised with respect to outcome and prognosis.
A subset of 35 genes that were independent from the morphology or karyotype of
the patients was selected; some of them are associated with the regulation of the
cell cycle or with apoptosis. By hierarchical cluster analysis, patients could be
classified into high-risk and low-risk groups with highly significant differences
in event-free survival (EFS) ( p 0.001).
Another approach was described by Qian et al. (21) in therapy-related AML
and myeloid cell lines focussing on CD34-positive selected cells. They were the
first ones to define a specific pattern of gene expression for t-AML in comparison
with other AML subtypes. The most discriminating genes were found to be
involved in arrested differentiation of early progenitor cells. A higher expression
of cell cycle control genes such as CCNA2, CCNE2, and CDC2 and genes for cell
cycle checkpoints such as BUB1 or growth (Myc) were found. Furthermore,
downregulation of transcription factors involved in early hematopoiesis (TAL1,
GATA1, EKLF) and overexpression of FLT3 was detected. The authors con-
cluded that these genes may be further investigated for new targets and drugs in
this very unfavourable subtype of AML.
As a further hallmark in AML, Bullinger et al. analyzed 65 peripheral
blood and 54 bone marrow samples in patients with AML (12). On the basis of
6283 most variably expressed genes they were able to reproduce cytogenetically
defined AML subgroups and, in addition, to define two different groups with
highly differing prognosis on the basis of gene expression profiles. While both
groups mainly included AML cases with normal karyotypes without differences
in many prognostic parameters, it is noteworthy that the group with the poorer

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prognosis included more patients with monosomy 7, complex aberrant
karyotypes, and length mutations of FLT3, while the group with the better
prognosis included more patients with inv(16). Thus, the observed differences in
the prognosis between both groups may be largely due to imbalances in profiles
of established prognostic factors rather than due to the identification of a newly
characterized biological subgroup of AML. Genes as published by Bullinger
et al. should be tested in independent cohorts of AML patients to further support
their prognostic power, and further investigations are again warranted for the
definition of such genes as new targets in AML treatment.
Similar results have been reported by Valk et al. (11) who discovered
16 groups of AML featuring distinct gene expression profiles on the basis of
microarray analysis, which, in addition, showed significant differences in clin-
ical course. However, while many of the identified groups were characterized by
specific cytogenetic aberrations known to be highly predictive of outcome, none
of the groups were restricted to cases without cytogenetic abnormalities. Thus,
the task remains to identify markers capable of discriminating prognostically
different cases out of the heterogeneous group of AML with normal karyotype
and to use these for target testing.
An improvement in this direction has been reported by Kern et al. who
analyzed gene expression profiles in 205 patients with AML and normal karyotype
(22). In order to identify genetically defined subgroups, an unsupervised principal
component analysis revealed 79% of cases clustering together, while a subgroup
comprising 21% of cases formed another cluster. Importantly, the analysis of
known genetic markers, including the presence of length mutations and point
mutations of FLT3, partial tandem duplications of MLL, or mutations of CEBPA,
NRAS, or CKIT, did not reveal differences between both groups. Significant
differences were found, however, in their phenotypes with more monocytic fea-
tures in the smaller group. Analysis of differentially regulated genetic pathways
revealed CD14, WT1, MYCN, HCK, and SPTBN1 as discriminating genes.
Stressing the potential impact of this analysis on the clinical management of AML,
these two groups significantly differed in the EFS. Thus, it was demonstrated here
also that within the group of AML with normal karyotype highly needed novel
molecular markers with prognostic impact can be identified by using gene
expression profiling. Some of the discriminating structures defined here may also
be used for future targets in specific AML subtypes.
However, regarding the biological heterogeneity of AML in general and of
AML with normal karyotype in particular, it is anticipated that further large-
scale studies in the context of clinical trials are needed to fully characterize and
validate novel and clinically relevant subgroups in AML and by doing so to
define new targets for individual treatment. A recent example is the study of
Bullinger et al. who further subclassified 93 patients with core binding factor
(CBF) leukemias (AML1-ETO and CBFB-MYH11) in different risk groups (23).
Another structure identified by gene expression profiling is the ubiquitin-
activating enzyme E1-like (UBE1) gene that is induced by all-trans retinoic acid
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(ATRA) in NB4 cells (24). Detailed investigation revealed that ATRA activates
the UBE1 promoter and the overexpression of UBE1 therefore triggers the
degradation of promyelocytic leukemia-retinoic acid receptor alpha (PML-
RARa) and leads to apoptosis in acute promyelocytic leukemia (APL) cells (25).
Clinical studies with UBE1 in leukemia are however missing.
Andersson et al. in a recent study compared (26) the molecular signatures
in childhood acute leukemias and their correlations to expression patterns in
normal hematopoietic subpopulations. 87 B-lineage acute lymphoblastic leukemia
(ALL), 11 T-cell ALL, 23 AML, and 6 normal bone marrows, as well as 10
normal hematopoietic subpopulations of different lineages and maturations were
ascertained by 27K cDNA microarrays. Not surprisingly, segregation according
to lineage and primary genetic changes was achieved. However, several genes
were identified that were preferentially expressed by the leukemic cells and not
by their normal counterparts. These genes suggest an ectopic activation and are
likely to reflect regulatory networks that may provide attractive targets for future
directed therapies. However, although this study clearly points to the right
direction, targets that were defined in this study have to be tested in an inde-
pendent cohort of patients before they may be used for drug design. This again
demonstrates that even if a variety of markers can be defined by gene expression
signatures in addition to the diagnostic pattern of a specific leukemia subtype,
the use of such information to find therapeutic structures or even targets is still
limited, which emphasizes the need for better support of translational research
and drug development in the future.
A possible approach to use expression profiling in a high-throughput
screening was published by Stegmaier et al. (27). They used HL-60 cells in
384-well culture plates and cultivated them with uniform concentrations of
1739 compounds to induce differentiation. By including different gene expression
signatures of AML-versus-monocyte and AML-versus-neutrophil distinctions as
measured by DNA microarrays, data were complemented by reverse transcription-
polymerase chain reaction (RT-PCR) and matrix assisted laser desorption/
ionization time-of-flight (MALDI-TOF). Because of this approach, finally eight
compounds were identified that reliably induced the differentiation signature. As a
result, a modest number of genes were sufficient to capture a complex cellular
response. However, the authors concluded that further investigations are needed to
identify the optimal gene signature. This work points to a possible scenario for the
identification of new targets and drugs by gene expression profiling. However, it
again demonstrates the complex problem to combine different highly sophisticated
methods in a high-throughput investigation to define at the end drugs to be tested
in a clinical trial.
Detection of New Targets in ALL
For sure, one of the most important questions posed by the use of gene expression
profiling is the identification of new targets for the further development of highly

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specific antileukemic drugs. One striking example is based on the results from
Armstrong et al. who found mutations and high-level expression in the FLT3
tyrosine kinase receptor gene in MLL-rearranged ALLs (28,29). FLT3 is known as
a tyrosine kinase receptor that is frequently activated by mutations in patients with
AML (30) but is rarely activated in ALL. However, by gene expression profiling it
was demonstrated that FLT3 was the gene most strongly associated with the
presence of MLL gene rearrangements in ALL. This leads to the idea (28,31,32) to
further investigate the potential role of this oncogene in the pathogenesis of MLL
tumors per se. Mutational analyses of FLT3 in MLL gene–rearranged leukemias
clearly showed the presence of activating mutations in the activation loop of this
tyrosine kinase receptor in 5 of 30 cases studied. This was further validated by
treating leukemia cells with PKC412, a specific inhibitor of the FLT3 tyrosine
kinase. It was shown both in vivo and in vitro that PKC412 has differential cytotoxic
effects on MLL rearranged leukemia cells harbouring FLT3 activation (28).
Furthermore, it was demonstrated that also in ALL with hyperdiploid
cytogenetics, the FLT3 receptor is frequently expressed at a higher level. This
again reinforces the value of gene expression profiling as a powerful approach
for the identification of novel drugs also in ALL (32–34), which should motivate
an urgent translation into clinical trials including high-risk patients.
Another approach in ALL to use gene expression for further insights in
biology of the disease was described by Zaza et al. (35). After intravenous
administration of thioguanine nucleotide (TGN), the TGN concentration was
determined in the leukemic blasts of 82 children with newly diagnosed ALL.
After analyses of 9600 genes, they identified 60 probes that were significantly
associated with TGN accumulation if patients were treated with mercaptopurine
(MP) alone and another 75 genes in patients treated with a combination of
metotrexate (MTX) and MP. There was no overlap between these two sets of
genes. The investigation was performed in parallel in vivo and in vitro and gene
expression profiling led to new insights into the genomic basis of interpatient
differences with respect to different treatment options. Through gene expression
profiling, clear correlations between a specific drug’s level in vivo and increased
expression of specific genes were detected. It was even visible that expression
profiles correlated to mono or combined treatment modalities. Prospective
studies are needed to test these results.
Another outstanding investigation was conducted by Holleman et al. (36)
who identified a set of differentially expressed genes in B-lineage ALL being
sensitive or resistant to several drugs such as prednisolone (33 genes), vincristine
(40 genes), asparaginase (35 genes), and daunorubicin (20 genes). A score of genes
combined to define overall sensitivity or resistance to all four drugs was tested in a
multivariate analysis and predicted outcome of 173 children investigated
( p ¼ 0.027). Although these genes do not per se define new targets of treatment,
gene expression profiling clearly demonstrated in a prospective setting which
treatment may or may not be successful. This may serve as an example for the
application of gene expression profiling to improve treatment and to define targets
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and drugs against these targets in ALL. The authors further point to the aspect that
it may be important to determine whether specific modulation of proteins encoded
by genes that were found may describe treatment response best. These proteins may
also point to previously unrecognized potential targets and new agents to augment
the efficacy of current chemotherapy for ALL.
Brown et al. (37) investigated FLT3 inhibition by the selective inhibitor
CEP-701 in ALL. In this study eight ALL cell lines and primary ALL cells from
39 patients were evaluated and a high potency for this drug especially in ALL cells
overexpressing FLT3, i.e., MLL rearranged cases, as well as ALLs with hyper-
diploid karyotypes was identified. Seven of seven sensitive samples examined by
immunoblotting demonstrated constitutively phosphorylated FLT3 that was
potently inhibited by CEP-701, whereas zero out of six resistant samples expressed
constitutively phosphorylated FLT3. The authors concluded that the compound
CEP-701, a potent and selective FLT3 inhibitor, effectively suppresses FLT3-
driven leukemic cell survival and clinical testing of this compound as a novel
molecularly targeted agent for treatment of ALL is warranted.
However, in most cases the candidate targets identified in expression
studies (28) using relapse or treatment outcome as endpoints of their observation
and independent verification is missing (38). Therefore, conflicting results are
largely due to differences in treatment and biology of enrolled patients. The gap
between gene expression profiling to characterize biological entities in leukemia
and lymphoma and the targets to be tested is still not closed, and translation from
data management to drug design is still missing. However, the characterization
of molecular mutations and of pathway alterations in the leukemias proceeds
with high velocity as can be demonstrated by the recent study of Mullighan et al.
who revealed the PAX gene as the most frequent target of molecular mutation in
ALL and showed that direct disruption of pathways controlling B-cell devel-
opment and differentiation contribute to B-progenitor ALL pathogenesis (39).
This is just one more example of the recent progress in the identification of new
molecular targets in ALL.
Detection of New Targets in Chronic Myeloid Leukemia
McLean et al. (40) intended to define specific gene expression profiles in chronic
myeloid leukemia (CML) patients all treated with imatinib. In correlation to
cytogenetic response data, the expression pattern of a subset of 55 out of more
than 12,000 genes was identified that best predicted response to therapy. The
sensitivity to predict the individual response was 93.4%; however, the specificity
was only 58.3%. The authors further found that many of the genes identified
appeared to be strongly related to BCR-ABL transformation mechanisms. Thus,
these genes may need further investigation as potential new drug targets in CML.
Diaz-Blanco et al. described several novel transcriptional changes in pri-
mary CD34 positive CML cells in comparison with normal CD34-positive cells
including an upregulation of components of the TGFB signaling pathway or

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candidate genes such as the leptin receptor (LEPR), thrombin receptor (PAR1),
or the neuroepithelial cell transforming gene 1 (NET1) (41). It was further
possible to define differentially regulated candidate genes discriminating chronic
from blast phase of CML such as PRAME (preferentially expressed antigen of
melanoma) (42) or CAMPATH (CD52) (43) or deregulation of pathways, e.g.,
the WNTB catenin signaling system (42). These studies thus might be helpful for
definition of new novel stem or progentior cell-associated targets and of
mechanisms being responsible for the higher malignant transformation of CML
(41,42).
Detection of New Targets in Chronic Lymphocytic Leukemia
One highlight to establish the use of gene expression profiling to define new
targets in leukemia was the detection of ZAP70 to be expressed in a large
proportion of chronic lymphocytic leukemia (CLL) (14). As the expression of
ZAP70 was high in IgVH-unmutated cases of CLL, this gene was further cor-
related to distinction within CLL cases with respect to prognosis. This finding
also led to the investigation of the ZAP70 antigen expression by antibodies in
CLL using multiparameter immunophenotyping (44). Recently, it was demon-
strated that ZAP70 can also be successfully screened by a quantitative RT-PCR
method (45). After definition of CLL signature genes, the protein products of
these genes may represent such new targets for monoclonal antibodies or for
vaccine approaches. Another aspect detected in this investigation was the fact
that B-cell activation genes were upregulated in Ig-unmutated patients. Thus,
pathways downstream of the B-cell receptor may contribute to aggressive clinical
cases. It may be beneficial to target these signaling pathways.
However, again, gene expression profiling so far was helpful in finding
new epitopes in strict correlation to a specific disease or even subgroups within
such diseases, but targeted drugs are still under investigation.
Future Investigations to Diagnostic and Therapeutic Use
of Gene Expression Profiling: The MILE Study
The (microarray innovations in leukemia) MILE study is a cooperation of the
European Leukemia Network (ELN, work package 13) together with Roche
Molecular Systems. This innovative study was designed to test microarrays in
parallel to gold standard diagnostics in 4000 patients with leukemia in 11 different
sites (7 from ELN, 3 in United States, 1 in Singapore). At least 18 different classes
of leukemia shall prospectively be defined for diagnostic use in the MILE study
by their respective gene expression signatures. The ELN work package 13 is
per se the head of these activities.
In order to set up a clearly defined study with comparable sample quality,
as a first step, a prephase was conducted to harmonize laboratory workflows.
This prephase included tests of similar aliquots of two cell lines and three
14 Haferlach et al.

22]
leukemia samples—AML, CML, and CLL. A first interim analysis was able to
demonstrate a very high inter- and intra-laboratory reproducibility (46). Figure 3
is an example of the data generated.
Stage I of the study now includes 2000 samples of leukemia all analyzed in
parallel with gold standard methods. After clarification of discrepant results
between gene expression analyses and gold standard report forms, the most
discriminating genes will be used to design a specific custom microarray for the
diagnosis of leukemias. This new microarray will then be tested prospectively in
stage II of the study by including another set of 2000 leukemia samples.
It is further intended to use a subset of this data to address further questions
like response to specific treatment as many patients are enrolled in prospective
clinical trials. Only studies like this may define new targets for treatment, because
information will be available on diagnosis, prognostic parameters, treatment, and
response as well as ultimately for treatment outcome. The power of gene expression
profiling may help in approaching such data sets from different perspectives and
may therefore be used to address several questions in parallel.
SUMMARY AND FUTURE TRENDS
As new drugs are classically tested in clinical trials, this may be an interesting
scenario for further use of microarrays. In many early clinical phase I/II studies
response rate is low and many patients have received some other treatment
before. However, if one is coupling clinical trials with gene expression profiling,
the investigators may enhance their information, as the identification of specific
gene expression profiles may correlate to drug response or resistance of the
individual patient. Products of such differentially expressed genes represent at
least plausible targets for inhibitors that may reverse the drug-resistance
Figure 3 Example for inter-laboratory reproducibility in MILE study: Center 7 versus all
other nine centers was calculated, all genes (38,000) were included in calculation.

22]
phenotype. Thus, these markers may be prospectively used to identify those
patients who are likely to respond to the new agent. In follow-up studies far
fewer patients would then be required to prove efficacy (38,47–50).
Today, we are on the way to design and to use specifically developed
microarrays with thousands of genes for the subclassification of leukemias and
lymphomas. The ongoing MILE study is one example of an international
approach to use gene expression profiling for the first time in a routine diag-
nostic setting. Also, a lymphoma microarray is already investigated for diagnosis
(51). Importantly, this information always includes information about a patient’s
prognosis, as clearly defined biological entities in leukemia and lymphoma lead
to disease-specific treatment and data are therefore also related to prognosis and
outcome. Some examples for this thesis are APL to be treated with ATRA or
arsenic trioxide, or BCR-ABL-positive leukemias that can be specifically treated
with imatinib or other tyrosine kinase inhibitors. Other examples are the use of
CD33-targeted treatment in AML with gemtuzumab ozogamicin, or anti-CD20
and anti-CD52 antibody-related treatment in lymphomas. In addition, several
studies were able to define a subset of genes that are not linked to a diagnostic
profile but can be also used for outcome prediction. These studies can even
demonstrate different marker genes that predict response to specific drugs.
So far, one has to accept that much less is known about the use of gene
expression profiling in finding new targets in leukemia and lymphoma. One nice
example may be the detection of ZAP70 in CLL that not only predicts the IgVH
status of the disease but can also be used as an antibody target to discriminate
patients at diagnosis. However, new treatment opportunities have not been
developed for this gene so far. Of course this does not mean that gene expression
profiling will never add information for new targets. By identifying new players
and pathways for resistance to therapy, DNA repair, and apoptosis, microarrays
open up new avenues for any targeted therapy that had not even existed a few
years ago. There is no evidence for any other technique today with so much
power for specific and less toxic treatment for cancer patients in the future.
However, the exact definition of the difference between a normal and a cancer
cell in all details is essentially required for the solution. The goal must be to
diagnose and stratify patients according to their disease-specific gene expression
profile before treatment starts and to treat individually with drugs specific for
such clearly defined biological entities. This does not mean that these drugs will
be individually defined for each patient but for a newly defined disease not based
only on morphology or cytogenetic parameters.
Models for the development of new targets in leukemia and lymphoma
should be adapted to large-scale clinical trials and have to focus in detail on new
medications tested. Thus, strong links between academic and industry initiatives
are urgently needed (52,53) to be the driving force behind the science. As cancer
pathways such as Ras, Src, or Myc are known and can be linked to several
tumors, their interaction and involvement can be studied by gene expression
profiling best. Therefore, not only single genes being over- or underexpressed
16 Haferlach et al.

22]
but altered pathways in leukemia and lymphoma also may lead to new targets in
the near future.
CLINICAL PERSPECTIVES FOR THE NEXT FIVE YEARS
Following its fast integration in hematological research we can expect gene
expression profiling to be included in clinical procedures already in the very near
future.
First, it might soon support the classification and risk stratification of
hematological malignancies as it provides a high degree of correlation with other
diagnostic methods such as flow cytometry (54,55) or PCR (56) and shows a
high diagnostic accuracy and reproducibility (7,57). The robustness of the
method is a further argument for its applicability in the clinical field (58).
Moreover, gene expression profiling is able to further subclassify distinct entities
such as chronic myelomonocytic leukemia, which could not be previously
subdivided by classical techniques (59). Although it is improbable that the new
technique will substitute all established methods such as cytomorphology,
cytogenetics, or PCR in the next years, it has to be expected that gene expression
profiling will become part of the diagnostic panel of hematological malignancies
and will be increasingly correlated with other methods or support those in case of
difficult differential diagnoses or decisions.
Second, a further step in the near future might be the inclusion in minimal
residual disease strategies. In combination with real-time PCR gene expression
profiling is able to serve for the definition of molecular markers, which can be
monitored during follow-up of the disease. This might be exemplified in AML in
the WT1 and PRAME genes (60).
Third, gene expression profiling will probably find its way in individu-
alized treatment planning as specific gene expression signatures are associated
with poor chemotherapy response and with drug resistance. These processes are,
e.g., mediated by a transcriptional program active in hematopoietic stem and
progenitor cells as was demonstrated in AML (61) and being associated with
nucleotide metabolism, apoptosis, and oxygen species metabolism (62). The
finding of such signatures therefore might be an indication for immediate
planning of allogeneic stem cell transplantation. However, such application of
gene expression profiling for the definition of chemosensitivity for individu-
alized treatment planning will probably have to be prepared somewhat longer
than the above mentioned indications and will probably be only part of research
studies rather than of routine strategies in the near future.
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duction/z_3B2_3D_files/978-0-8493-5083-2_CH0002_O.3d] [7/4/08/15:32:42]
[23–44]
2
Individualized Tumor Response Testing
in Leukemia and Lymphoma
Andrew G. Bosanquet
Bath Cancer Research, Royal United Hospital, Bath, U.K.
Peter Nygren
Department of Oncology, Radiology, and Clinical Immunology,
University Hospital, Uppsala, Sweden
Larry M. Weisenthal
Weisenthal Cancer Group, Huntington Beach, California, U.S.A.
INTRODUCTION
Individualized tumor response testing (ITRT) has a long history, with a number
of different technologies and many different tumor types tested. Almost all
technologies used for hematological malignancies are identical in their logic and
similar in their execution. The concepts underlying cell death assays are rela-
tively simple, even though the technical features and data interpretation can be
complex. The logic is that if the drug kills tumor cells from an individual patient
in a ‘‘test tube,’’ then it is more likely to be effective when administered directly
to a patient. Conversely, a drug that does not kill the patient’s cells, even at
concentrations significantly higher than can be achieved in the patient, is
unlikely to be effective. Considerable work based on these assays has been
reported during the past 25 years, and recently an ad hoc group of 50 scientists
from 10 countries agreed on the term ‘‘individualized tumor response’’ for these
23

[23–44]
tests, describing them as the ‘‘effect of anticancer treatments on whole living
tumor cells freshly removed from cancer patients’’ and not including tests with
‘‘subcellular fractions, animals or cell lines’’ (1).
We present results for hematological neoplasms, but note that analogous
results have been published for a variety of solid tumors in substantial numbers
of patients (2).
TOTAL CELL KILL/CELL DEATH ASSAYS
There is a clear divide between the two main technologies used in this work: an
ITRT endpoint can be based either on reduction of cell proliferation or on cell
death (3–6). Historically, the cell proliferation endpoint received great attention
as a result of studies by Salmon, Von Hoff, and others during the late 1970s and
early 1980s (7,8). These studies occurred during the heyday of the oncogene dis-
covery period in cancer research, when oncogene products were frequently found to
be associated with cell growth and when cancer was most prominently considered to
be a disease of disordered cell growth. In contrast, the concept of apoptosis had yet
to become widely recognized. Also unrecognized were the concepts that cancer may
be a disease of disordered apoptosis/cell death and that the mechanisms of action of
most, if not all, available anticancer drugs are mediated through apoptosis. When
problems with cell proliferation assays emerged (9,10), there was little enthusiasm
for studying cell death as an alternative endpoint (11). These factors explain many
abandoning research into ITRT during the 1980s.
As opposed to measuring cell proliferation, there is a family of assays
based on the concept of total cell kill or, in other words, cell death occurring in
the entire population of tumor cells (3–6).
The basic technology concepts are straightforward. Cells are isolated from
a fresh specimen obtained from a viable neoplasm. These cells are cultured in the
continuous presence or absence of a drug, most often for three to seven days. At
the end of the culture period, a measurement is made of cell injury, which
correlates directly with cell death, almost always by apoptosis (12–14).
Although there are methods for specifically measuring apoptosis per se,
there are practical difficulties in applying these methods to mixed (and some-
times clumpy) preparations of tumor cells and normal cells. Thus, more general
measurements of cell death have been applied. One of these measurements is the
delayed loss of cell membrane integrity, which has been found to be a useful
surrogate for apoptosis. This is measured by differential staining in the Differ-
ential Staining Cytotoxicity (DiSC) assay method, which allows selective drug
effects against tumor cells to be recognized in a mixed population of tumor and
normal cells (6,15). More recently the Tumor Response to Antineoplastic
Compounds (TRAC) assay was described as a streamlined version of the DiSC
assay (16). Other cell death endpoints include loss of mitochondrial Krebs cycle
activity, as measured in the Methylthiazol Tetrazolium (MTT) assay (17), loss of
cellular adenosine triphosphate (ATP), as measured in the ATP assay (18), and
24 Bosanquet et al.

[23–44]
loss of cytosolic esterase activity and cell membrane integrity, as measured by
the Fluorometric Microculture Cytotoxicity Assay (FMCA) and similar assays
(19–21). Most recently, other methods including assays to measure apoptosis
more specifically have been described, although it remains to be seen if these
will offer any real advantages over the other measurements of cell death (22–26).
These four endpoints produce valid and reliable measurements of cell
death. They also correlate well with each other on direct comparisons of the
different methods (17,19,20,27–29). For instance, Weisenthal and associates have
performed direct correlations between the DiSC and MTT assays in approximately
5,500 fresh human tumor specimens, testing an average of 15 drugs per specimen
at two different concentrations. Although these endpoints agree with each other
in most solid tumors (overall correlation coefficient ¼ 0.85), we consider that
the MTT assay is more problematic in hematological neoplasms. For example,
correlations between treatment outcomes and assay results have been more
consistent in acute nonlymphocytic leukemia (ANLL) with the DiSC assay
endpoint (30–32) than with the MTT endpoint (22,33,34).
Additionally, there is a clear relationship between prior treatment status
and assay results for anthracyclines in the case of the DiSC assay (relapsed
patients having blast cells that are clearly more resistant than those in previously
untreated patients, Table 1), which was not evident when ANLL was tested with
the MTT assay (35). The absolute magnitudes of drug effects (cell kill) are
substantially greater when scored in the DiSC assay than in the MTT assay in the
case of ANLL (Table 1). Finally, the correlation coefficient between DiSC and
MTT assays was weaker in the case of ANLL (median r ¼ 0.75), than in other
classes of neoplasms that Weisenthal had tested (median r ¼ 0.85).
There are at least two explanations for the greater drug effects detected in
the DiSC endpoint.
Firstly, the DiSC assay is a more specific endpoint for drug effects on blast
cells (as opposed to drug effects on blast cells plus the normal cells frequently
present in ANLL specimens).
Table 1 In Vitro Activity of Anthracyclines in ANLL As a Function of Prior Treatment
Status and Individualized Tumor Response Testing Endpoint
Drug/assay
Number
untreated
Number
treated
Cell fraction
surviving
(untreated)
Cell fraction
surviving
(relapsed) P
Doxorubicin/DiSC 12 16 0.11 0.33 0.020
Doxorubicin/MTT 12 16 0.34 0.42 0.428
Idarubicin/DiSC 10 16 0.06 0.25 0.0015
Idarubicin/MTT 10 16 0.35 0.45 0.180
Abbreviations: ANLL, acute nonlymphocytic leukemia; DiSC, differential staining cytotoxicity;
MTT, methylthiazol tetrazolium.
Source: From Ref. 35.
Individualized Tumor Response Testing in Leukemia and Lymphoma 25

[23–44]
Secondly, it takes longer for cells to lose the ability to produce a signal in
the MTT assay than it does for them to be scored as dead in the DiSC assay
[e.g. (36)]. It is possible that the MTT assay would be more useful in ANLL (i)
were it applied only in cases in which there was a ‘‘pure’’ (90%) population of
blast cells at the end of the assay, and/or (ii) were the duration of the cell culture
(and drug exposure) extended beyond the typical 96-hour period of these assays.
With regard to the first of these latter possibilities, it is notable that Hongo, et al.
(who contributed a disproportionate share of weak clinical correlations in Table 2)
did not attempt to determine the percentage of blast cells at the time the MTT
endpoint was measured (34).
A final point of emphasis is that it is important to rigorously standardize
assay conditions, including precisely controlling the duration of drug exposure
and cell culture. Thus, the DiSC assay and similar tests have some advantages
over the other short-term assays.
COMPLETED STUDIES OF CORRELATION BETWEEN CELL
DEATH ASSAY RESULTS AND CHEMOTHERAPY RESPONSE
As with other laboratory tests, the determination of the efficacy of ITRT is based
on comparisons of laboratory results with patient response (commonly referred
to as ‘‘clinical correlations’’). The hypothesis to be tested with clinical corre-
lations is a simple one—that above-average drug effects in the assays correlate
with above-average drug effects in the patient, as measured by both response
rates and patient survival.
Table 2 and Figure 1 show that, with respect to response, the above
hypothesis has been confirmed to be true in all published studies. At each point
in the distribution of overall response rates, patients with test results in the
‘‘sensitive’’ range were more likely to respond than the total patient population
as a whole. Conversely, patients with test results in the ‘‘resistant’’ range were
less likely to respond than the patient population as a whole. On average, patients
with assays in the test sensitive range were threefold more likely to respond than
patients with assays in the test resistant range (see the ‘‘Overall relative risk’’
column in Table 2).
Considering this evidence as a whole, can it be inferred with confidence
that the cell death measured in the assays correlates with tumor cell death
measured in the patient? Comparing the chronic lymphocytic leukemia (CLL)
and acute lymphoblastic leukemia (ALL) data with the more limited but also
consistent data in non-Hodgkin’s lymphoma (NHL), a powerful case is made to
support the clinical relevance of this testing in human lymphatic neoplasms.
Considering the ANLL data in the context of the lymphatic neoplasm data, a
powerful case is made to support the clinical relevance of this testing in hem-
atological neoplasms in general.
The body of literature supporting cell death assays in lymphatic neoplasms
dates to studies in CLL published by Schrek in the 1960s (37,38). Schrek
26 Bosanquet et al.

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