ALBERT

Description: Early trials evaluating nonmyeloablative allogeneic hematopoietic transplant (NST) for metastatic renal cell carcinoma (RCC) have reported widely discordant results. We performed a meta-analysis of the published literature to identify prognostic factors for NST. However, known RCC prognostic factors are not often detailed in these studies, hindering the identification of patient differences among studies. We explored the order of patient entry as a surrogate marker for patient selection, hypothesizing that patient selection, rather than treatment differences, accounted for early promising results. Patient specific data and adequate follow-up were available for 76 patients from six studies. The rank of patient entry on each individual trial was recorded as well as ≥ grade 2 acute graft-versus-host disease (aGVHD), recipient age, recipient sex, response (partial plus complete), and survival. Multivariable analyses for response and survival were modeled using logistic and cox proportional hazards regression, respectively. The mean overall response rate was 22/76 (29%), with responses across individual studies significantly varying from 0% to 57% P =.009 by Chi-square). Median Kaplan-Meier survival was 263 days. Neither age nor sex was significantly ( associated with response or survival. Acute GVHD occurred in 42% of individuals and correlated with response (OR=9.9, P =0.012) but not survival (HR=1.53, P=0.28). When adjusting by study, later patient entry rank reduced the probability of response (OR=0.30, P =0.007) and survival (HR=1.89, P =0.006). Alternatively, being among the first five patients enrolled in a given study relative to subsequent patients, increased the probability of response (OR=6.69, 95% CI 1.95–39.1, P=0.005), even when adjusting for aGVHD, and afforded a survival benefit (HR= 0.45, 95% CI 0.022 −0.92, P=0.028). The prognostic strength of patient entry rank strongly suggests “entry bias” in patient selection. This bias potentially accounts for the large variation in outcome among studies and for the promising results in early studies. Entry bias analysis offers a novel method to assess early phase trials for selection bias, when detailed individual prognostic information is lacking. Further study is warranted to determine to what extent, if any, entry bias occurs in other clinical trial settings. Figure Figure

Description: Chromosome translocations are among the most common genetic abnormalities in human leukemia. Each translocation may affect a different pair of genes. The abnormally expressed genes that result from the different translocations provide a rich source for identifying specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow (BM) samples from 22 patients with four types of AML, namely de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11).We generated SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patients’ samples and normal CD15+ BM; they were also statistically significantly different at the 5 % level. Using these strict criteria, we identified 1,571 unique tags, of which 1,405 were known genes and ESTs, and 166 were novel transcripts that were either specific for each translocation or were common for all four translocations. Changes in expression of these known genes which fall into different gene ontogeny functional categories varied by translocation. For example, those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). Cell surface receptor signaling, intracellular signaling and RNA processing were altered in treatment related but not in de novo t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial pilot microarray experiment with 96 genes that were specific for each translocation or common for all translocations used mononuclear cells from normal and patient BM and translocation cell lines, ME-1, THP-1, Mono Mac-6, Kasumi 1, NB-4; the array data from BM matched the SAGE data for 48-75 % of genes and the majority of cell lines, except ME-1, matched at least 70 % with the SAGE results for the appropriate translocation. We have now designed a full-scale microarray that contains over 400 probes including 250 known genes, 61 ESTs, 45 novel sequences and 48 genes reported by others. We will test at least 100 patients’ samples with the four translocations to validate which genes provide a robust, reproducible “fingerprint” for each translocation and for all translocations. We will correlate our microarray data with age, sex, race, response to treatment, survival and other mutations (FLT3, MLL ITD, etc) to identify any transcripts that might reliably define these categories. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype.