Predicting clinical outcome through gene expression profiling in stage III melanoma

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 8502-8502
Author(s):  
T. John ◽  
M. A. Black ◽  
T. Toro ◽  
C. A. Gedye ◽  
I. D. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Stage Iiib ◽  
Stage Iii ◽  
Test Set ◽  
Prognostic Groups ◽  
Melanoma Patients ◽  
Stage Iii Melanoma

8502 Background: Melanoma patients with clinically involved regional lymph nodes (Stage IIIB&C) represent a prognostically heterogeneous population. Current prognostic factors cannot distinguish the 30% of patients who will achieve long term survival from those who will relapse early. We hypothesized that gene expression profiling could identify these different prognostic groups and provide a greater understanding of the genetic mechanisms involved. Methods: Lymph node sections from 29 patients with Stage IIIB & IIIC melanoma and divergent clinical outcome as defined by time to tumor progression (TTP), including 16 poor (TTP<6 months) and 13 good (TTP>28 months) prognosis patients, were subjected to molecular profiling using spotted oligonucleotide arrays containing 30,888 probes as an initial test set. The differentially expressed genes were determined using a Wilcoxon-Mann-Whitney t-test with the false discovery rate controlling method of Benjamini-Hochberg and validated using quantitative real-time RT-PCR. Using logistic regression, a predictive score algorithm was developed based on the 15 genes for which the correlation between the two platforms was the strongest. The score was then applied to two independent validation sets of 10 and 14 patient samples. Results: Supervised analysis using differentially expressed genes was able to distinguish the two prognostic groups in the test set. The score correlated directly with clinical outcome, with higher scores associated with improved TTP. When the score was then applied to two independent sets of Stage III melanoma patient samples, it predicted clinical outcome accurately in 90% of samples. Conclusions: Stage IIIB and IIIC melanoma can be prognostically sub-classified according to the expression of 15 genes. To our knowledge this is the first study focused on Stage III disease using ex vivo patient samples. These results are encouraging and this genetic signature is currently being validated on a larger cohort. This method will allow appropriate stratification of stage III melanoma patients in adjuvant clinical trials, ameliorating the inherent biological heterogeneity that can confound these studies. [Table: see text]

Gene Expression Profiling of Paired Pre- and Post-Prednisolone (PRED) BM Samples from Childhood ALL Identifies Robust Signatures for PRED Response and Eventual Outcome.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 222-222 ◽  
Author(s):  
Yi Lu ◽  
Huiqing Liu ◽  
Ying Xu ◽  
Pei Lin Koh ◽  
Ariffin Hany ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Response To Therapy ◽  
Test Set ◽  
Childhood All ◽  
Blast Count ◽  
Highly Correlated ◽  
Eventual Outcome

Abstract Early response to therapy is the most important prognostic factor for childhood ALL. CCG investigators have shown that Day-7 and Day-14 BM blast counts were prognostically important although there is great inter-observer variability. BFM group have shown that day 8 prednisolone (PRED) response is highly predictive of the treatment outcome. While gene expression profiling (GEP) of diagnostic marrow can discern a pattern of PRED sensitivity as determined by in vitro MTT assay, the accuracy was low at only 70%. We hypothesized that changes in global GEP after therapy have a higher likelihood to predict response as the signatures of sensitivity and resistance may be unmasked during the therapy. We prospectively studied the changes in GEP using Affymetrix HG-U133A or Plus 2 chips on paired BM samples before and after 7-day course of PRED and one dose IT MTX in 58 patients with newly diagnosed or relapsed ALL. Unsupervised hierarchical clustering revealed that pre- and post- PRED samples in the patients still tended to cluster together, indicating that expression profiles of molecular subgroups were still most important. To remove intrinsic influence of molecular subtypes and identify potential signatures independent of genetic abnormalities, we subtracted Day-0 GEP from its paired Day-8 profile and retained probe sets with significant changes (≥ 10-fold). To avoid the ambiguity of variation in BM blast counting at Day-8, we divided the samples into a stringently reproducible group where “Good” PRED response was defined as that Day-8 blast count in PBL < 109/L and BM lymphoblasts ≤ 30% (n=16). “Poor” response was when Day 8 PBL ≥ 109/L (n=11). This stringently reproducible group (n=27) formed the training group to help define a distinct signature while the rest (n=31 pairs) were used as a blinded test set. 54 and 19 discriminating genes were identified by 2 independent statistical methods respectively, and an integrated predictor model was constructed based on shortlisted entries. This model predicted the PRED response with 100% accuracy for the training set using the leave-one-out cross validation but was less accurate in predicting the BM blast count in blinded test set. But intriguingly, in the blinded test set, this model predicted correctly 19 out of 21 reliable “Good” PRED responses are in CCR (91%), while among 8 predicted as “Poor” responses, only 2 are in CCR (25%). This suggests that as gene expression profiling as early as day 8 of PRED could discern the beginning of leukaemia cell death even before morphological changes are discernable and is highly correlated to eventual outcome. In conclusion, we have shown that analyses on the relative changes of gene expression profile can identify real genetic signatures indicating the sensitivity to PRED administration which is highly correlated with outcome.

Gene Expression Profiling Identifies a Prognostically Favorable Subgroup in AML Independent of Cytogenetic Stratification.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 569-569 ◽  
Author(s):  
Claudia Schoch ◽  
Wolfgang Kern ◽  
Alexander Kohlmann ◽  
Wolfgang Hiddemann ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Survival Rate ◽  
Clinical Outcome ◽  
Expression Profiling ◽  
Normal Karyotype ◽  
Overall Survival Rate ◽  
Training Set ◽  
Test Set ◽  
Genetic Abnormalities

Abstract Acute myeloid leukemia (AML) is a heterogeneous group of diseases with varying clinical outcome. So far the karyotype of the leukemic blasts as well as molecular genetic abnormalities - both abnormalities on the genomic level - have been proven to be strong prognostic markers. However, even in genetically well defined subgroups clinical outcome is not uniform and a large proportion of AML shows genetic abnormalities of yet unknown prognostic significance. Here we addressed the question whether gene expression profiles are associated with clinical outcome independent of the known genomic abnormalities. Therefore, gene expression analyses were performed using Affymetrix U133A+B oligonucleotide microarrays in a total of 403 AML treated uniformly in the AMLCG studies. This cohort was divided randomly into a training set (n=269) and a test set (n=134). The training set included 18 cases with t(15;17), 22 cases with t(8;21), 29 cases with inv(16), 14 cases with 11q23/MLL-rearrangement, 19 with complex aberrant karyotype and 167 cases with normal karyotype or “other” chromosome aberrations. The respective data for the test set were: 10 t(15;17), 8 t(8;21), 11 inv(16), 8 11q23/MLL, 19 cases with complex aberrant karyotype and 78 with normal karyotype or “other” chromosome aberrations. Based on the clinical outcome the training cohort was divided into 4 equally large subgroups. We trained support vector machines (SVM) with the training set and classified the cases of the test set with the respective most discriminating genes. Next a Kaplan-Meier analysis was performed with the test set cases assigned to prognostic groups 1 to 4 according to SVM classification. Based on the expression level of 100 genes group 1 showed an overall survival rate of 57% at 3 years. 31 of 134 (23%) patients were assigned to this favorable subgroup. They belonged to the following cytogenetic subgroups: t(15;17) n=6, t(8;21) n=4, inv(16) n=3, 11q23/MLL n=4, complex aberrant karyotype n=1 and normal karyotype or “other” chromosome aberration n=13. The overall survival rate of groups 2, 3, and 4 did not differ significantly (17%, 21%, and 19% at 3 years). Among the genes highly expressed in the favorable group were MPO and the transcription factor ATBF1, which regulates CCND1. The unfavorable groups were characterized by a higher expression of the transcription factors ETS2, RUNX1, TCF4, and FOXC1. Interestingly, 10 of the top 40 differentially expressed genes are involved in the TP53-CMYC-pathway with a higher expression of 9 of these in the unfavorable groups (SFRS1, TPD52, NRIP1, TFPI, UBL1, REC8L1, HSF2, ETS2 and RUNX1). In conclusion, gene expression profiling leads to the identification of prognostically important alterations of molecular pathways which have not yet been accounted for by use of cytogenetics. This approach is anticipated to help optimizing therapy for patients with AML.

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