Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias.

SummaryGene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r 2 correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability.

The diagnosis of myelodysplastic syndrome (MDS) currently relies primarily on the morphologic assessment of the patient's bone marrow and peripheral blood cells. Moreover, prognostic scoring systems rely on observer-dependent assessments of blast percentage and dysplasia. Gene expression profiling could enhance current diagnostic and prognostic systems by providing a set of standardized, objective gene signatures. Within the Microarray Innovations in LEukemia study, a diagnostic classification model was investigated to distinguish the distinct subclasses of pediatric and adult leukemia, as well as MDS. Overall, the accuracy of the diagnostic classification model for subtyping leukemia was approximately 93%, but this was not reflected for the MDS samples giving only approximately 50% accuracy. Discordant samples of MDS were classified either into acute myeloid leukemia (AML) or "none-of-thetargets" (neither leukemia nor MDS) categories. To clarify the discordant results, all submitted 174 MDS samples were externally reviewed, although this did not improve the molecular classification results. However, a significant correlation was noted between the AML and "none-ofthe-targets" categories and prognosis, leading to a prognostic classification model to predict for time-dependent probability of leukemic transformation. The prognostic classification model accurately discriminated patients with a rapid transformation to AML within 18 months from those with more indolent disease.

Summary. Radioimmunoassays for erythropoietin are limited so far to a few specialized laboratories and this requires transport and storage of samples. We therefore tested the stability of immunoreactive erythropoietin in serum and plasma samples obtained from a uremic and a nonuremic anemic patient. No significant change in the concentration of immunoreactive erythropoietin was found in either serum or plasma samples for up to 14 days of storage. This type of stability was observed no matter whether the samples were stored at room temperature, 4 ° C, or -20 ° C. There was no difference between the estimates of erythropoietin in serum and heparinized plasma. Validity of the radioimmunoassay used in this study was demonstrated by parallelism of dilution curves of test specimens and the 2 na International Reference Preparation for erythropoietin and by a close correlation between the immunoreactivity and the bioactivity of the hormone, as assessed in the same samples by the exhypoxic polycythemic mouse bioassay.In conclusion the data obtained clearly indicate that the necessity of storage and transport of clinical samples does not limit the practicability of the radioimmunoassay for erythropoietin.Key words: Erythropoietin -Stability -Radioimmunoassay -Polycythemic mouse bioassay -Recombinant DNA Since recombinant human erythropoietin (rhEPO) became available for replacement therapy in paAbbreviations. BSA = bovine serum, albumin; EPO = erythropoietin; irEPO=immunoreactive erythropoietin; IRP=Inter-national Reference Preparation; hct=hematocrit; rhEPO=re-combinant human erythropoietin; RIA = radioimmunoassay tients with certain forms of anemia [5,9], the determination of erythropoietin (EPO) levels in body fluids has gained increasing clinical importance. However, radioimmunoassay kits are not yet commercially available and the limitation of EPO determinations to specialized laboratories necessitates storage and transport of the samples. This makes it important to know the stability of EPO in human samples. We therefore performed subsequent radioimmunological determinations of EPO for up to 14 days on serum and plasma samples which were stored at room temperature, 4 ° C, or -20 ° C. The validity of RIA estimates was secured by determination of the biological activity of the same samples using an in vivo bioassay and by comparison of the slopes of dilution curves of test samples and the 2 nd International Reference Preparation (IRP) for EPO. Material and Methods Antiserum to Erythropoietin

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BACKGROUND:Gene expression profiling has the potential to offer consistent, objective diagnostic test results once a standardized protocol has been established. We investigated the robustness, precision, and reproducibility of microarray technology.

During the years of 2005 to 2008, the MILE (Microarray Innovations in LEukemia) study research program was performed in 11 laboratories across three continents: 7 from the European Leukemia Network (ELN, WP13), 3 from the US and 1 in Singapore. The first stage was designed as biomarker discovery phase to generate whole-genome gene expression profiles (GEP) from recognized categories of clinically relevant leukemias and myelodysplastic syndromes (MDS). These were C1: mature B-ALL with t(8;14), C2: pro-B-ALL with t(11q23)/MLL, C3: c-ALL/pre-B-ALL with t(9;22), C4: T-ALL, C5: ALL with t(12;21), C6: ALL with t(1;19), C7: ALL with hyperdiploid karyotype, C8: c-ALL/pre-B-ALL without specific genetic abnormalities, C9: AML with t(8;21), C10: AML with t(15;17), C11: AML with inv(16)/t(16;16), C12: AML with t(11q23)/MLL, C13: AML with normal karyotype or other abnormalities, C14: AML with complex aberrant karyotype, C15: CLL, C16: CML, C17: MDS, and C18: non-leukemic and healthy bone marrow samples as controls and were compared to conventional diagnostic assays (“Gold Standard”). Data from the completed MILE Stage I included 2143 retrospectively collected adult and pediatric samples tested with HG-U133 Plus 2.0 microarrays (Affymetrix). In total only 47 analyses (2.1%) failed technical quality criteria. Cross-validation accuracy (average of three 30-fold cross-validations) of the final 2096 MILE Stage I samples was 92.1% concordant with the center-specific “Gold Standard” diagnosis (average call rate 99.4%). In nine classes the sensitivity was ≥94.3%: C2, C3, C4, C5, C9, C10, C11, C15, and C16. Lower sensitivities were observed for C7, C8, C14, and C17; which can largely be explained by the biological heterogeneity and non-standardized “Gold Standard” definitions for these entities. Yet, it is notable that all these classes showed specificities above 98.1%. In order to assess the clinical utility of microarray-based diagnostics a prospective Stage II was subsequently performed using a customized microarray representing 1480 probe sets. Overall, 1156 high quality GEP have been generated in MILE Stage II and represent an independent and blinded test set for the algorithms developed. A focused classification scheme aimed at accurately addressing only acute leukemias resulted in a 95.5% median sensitivity and a 99.5% median specificity for the 14 classes included in the classifier (C1 – C14, n=696). Lower accuracies were observed for the interface of C7–C8 in ALL, as well as C12 and C14 in AML. Interestingly, during the process of discrepant results analyses, it was observed that for 7.5% (n=52) of acute leukemias microarray results were correctly diagnosing samples as compared to the initial “Gold Standard” diagnoses entered into the study database, either because of erroneous entries into case report forms (24%) or subsequent re-testing of left-over material following the suggested diagnosis from the microarray (76%). In addition, predicted accuracies for CLL, CML and MDS in Stage II were 99.2%, 95.2%, and 81.5%, respectively. In conclusion, the MILE research study confirms in a final cohort of 3252 patients that microarrays accurately classify acute and chronic leukemia samples into known diagnostic and prognostic sub-categories. This final report underlines that the standardized method of gene expression profiling with low technical failure rate and simplified standard operating procedures may improve current “Gold Standards” as an adjunct to conventional diagnostic algorithms and potentially offers a reliable diagnostic/prognostic tool for many patients who don’t have access to a state-of-the-art “Gold Standard” workup. Our gene expression database, intended to be submitted to the public domain, will further contribute to research that aims to elucidate the molecular understanding of leukemias.

Gene expression profiling has been used to distinguish two major subtypes of diffuse large B cell lymphoma (DLBCL), termed germinal center B cell-like (GCB) DLBCL and activated B cell-like (ABC) DLBCL. Following CHOP-like chemotherapy, GCB and ABC DLBCLs had distinct 5-year survival rates of ∼60% and ∼30%, respectively. Prognostic gene expression signatures in CHOP-treated DLBCL include the lymph node signature, which reflects a non-malignant host response, the MHC class II signature, both favorable when expressed and the proliferation signature which is adverse when expressed. The addition of rituximab to CHOP chemotherapy (R-CHOP) has significantly improved the outcome for DLBCL patients. We therefore investigated, if gene expression signatures that predicted survival among DLBCL patients treated with CHOP remained predictive for DLBCL patients treated with R-CHOP. Gene expression profiling was performed on 156 samples from previously untreated patients with DLBCL using Affymetrix U133 plus arrays. All patients received rituximab and CHOP-like chemotherapy. Samples were classified as GCB DLBCL, ABC DLBCL, or unclassified, and were assessed for expression of the lymph node and proliferation signatures. A Cox-proportional hazards model was used to determine the association of these gene expression features with overall survival (OS). 71 DLBCL samples were classified as GCB DLBCL, 63 as ABC DLBCL, and 22 were unclassified. The addition of rituximab improved OS for both GCB and ABC DLBCL compared to historical controls treated with CHOP-like chemotherapy alone. After a median follow-up of 2.3 years, GCB DLBCL had a more favorable OS than ABC DLBCL, with 3-year OS rates of 86% vs. 68% (p = 0.014). The 3-year OS rate of unclassified DLBCLs was 69%. The lymph node signature was associated with favorable OS (p = 0.023) and the proliferation signature with inferior OS (p = 0.009), whereas the MHC class II signature was not associated with OS (p = 0.44). In summary, addition of rituximab to CHOP-like chemotherapy improved OS for both GCB and ABC DLBCL but ABC DLBCL remained inferior to GCB DLBCL. The prognostic value of the lymph node and proliferation signatures were maintained in the context of R-CHOP therapy. An understanding of the biological attributes of DLBCL tumors that are reflected in these gene expression signatures remains critical to our ability to improve survival of these patients.