A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia.
OBJECTIVE
Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by a variable clinical course. Several parameters have prognostic capabilities but are associated with altered response to therapy in only a small subset of patients.


METHODS
We used gene expression profiling methods to generate predictors of therapy response and prognosis. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemoimmunotherapy were created using cancer cell lines and patient leukemia cell samples. We validated and applied these three signatures to independent clinical data from four cohorts, representing a total of 301 CLL patients.


RESULTS
A genomic signature of prognosis created from patient leukemic cell gene expression data coupled with clinical parameters significantly differentiated patients with stable disease from those with progressive disease in the training data set. The progression signature was validated in two independent data sets, showing a capacity to accurately identify patients at risk for progressive disease. In addition, genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were generated and could accurately distinguish responding and nonresponding CLL patients.


CONCLUSIONS
Thus, microarray analysis of CLL lymphocytes can be used to refine prognosis and predict response to different therapies. These results have implications for standard and investigational therapeutics in CLL patients.
Friedman
Daphne R
DR
Divisions of Hematology, Oncology, and Cellular Therapy, Duke Institute for Genome Sciences and Policy, Durham VA Medical Center, Durham, North Carolina, USA.
Weinberg
J Brice
JB
Barry
William T
WT
Goodman
Barbara K
BK
Volkheimer
Alicia D
AD
Bond
Karen M
KM
Chen
Youwei
Y
Jiang
Ning
N
Moore
Joseph O
JO
Gockerman
Jon P
JP
Diehl
Louis F
LF
Decastro
Carlos M
CM
Potti
Anil
A
Nevins
Joseph R
JR
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000911:Antibodies, Monoclonal / Q000494:pharmacology; D058846:Antibodies, Monoclonal, Murine-Derived; D000970:Antineoplastic Agents / Q000494:pharmacology; D002699:Chlorambucil / Q000494:pharmacology; D003520:Cyclophosphamide / Q000494:pharmacology; D018450:Disease Progression; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods; D006801:Humans; D007167:Immunotherapy / Q000379:methods; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000235:genetics* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015649:Pentostatin / Q000494:pharmacology; D010597:Pharmacogenetics / Q000379:methods; D011379:Prognosis; D012306:Risk; D000069283:Rituximab
D000911:Antibodies, Monoclonal; D058846:Antibodies, Monoclonal, Murine-Derived; D000970:Antineoplastic Agents; D002699:Chlorambucil; D015649:Pentostatin; D000069283:Rituximab; D003520:Cyclophosphamide